https://airwiki.elet.polimi.it/api.php?action=feedcontributions&user=PaoloCalloni&feedformat=atomAIRWiki - User contributions [en]2024-03-29T09:44:56ZUser contributionsMediaWiki 1.25.6https://airwiki.elet.polimi.it/index.php?title=Brain-Computer_Interface&diff=9568Brain-Computer Interface2009-12-07T18:31:26Z<p>PaoloCalloni: </p>
<hr />
<div>A Brain-Computer Interface (BCI) is an experimental communication system that allows an individual to control a device by using signals from the brain (e.g., electroencephalography -- EEG).<br />
<br />
You can find a longer description on the [http://airlab.elet.polimi.it/index.php/airlab/research_areas/biosignal_analysis?z=2299 AIRLab page].<br />
<br />
The BCI project is in the [[BioSignal_Analysis]] area.<br />
<br />
== Ongoing projects ==<br />
<br />
* [[A genetic algorithm for automatic feature extraction from EEG data]]<br />
* [[Graphical user interface for an autonomous wheelchair]]<br />
* [[Online automatic tuning of the number of repetitions in a P300-based BCI]]<br />
<br />
== New projects ==<br />
There are various proposal for students interested in projects/thesis in the field of brain-computer interfaces:<br />
*[[First Level Course Projects#Brain-Computer_Interface|First Level Course Projects]]<br />
*[[First Level Theses#Brain-Computer_Interface|First Level Theses]]<br />
*[[Master Level Course Projects#Brain-Computer_Interface|Master Level Course Projects]]<br />
*[[Master Level Theses#Brain-Computer_Interface|Master Level Theses]]<br />
<br />
== Finished projects ==<br />
<br />
* [[Predictive BCI Speller based on Motor Imagery]] (Master thesis, Tiziano D'Albis)<br />
* [[Feature Selection and Extraction for a BCI based on motor imagery]] (Master thesis, Francesco Amenta)<br />
* [[Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface]] (Master Thesis, Paolo Calloni)<br />
* [[Ocular Artifacts Filter implementation for a BCI based on motor imagery]] (First Level thesis, Fabio Beltramini)<br />
* [[Reproduction of an algorithm for the recognition of error potentials]]<br />
* [[Online P300 and ErrP recognition with BCI2000]] (Master thesis, Andrea Sgarlata).<br />
* Tesi di Carlo Gimondi e Luisella Messana <br />
* Tesi di Gianmaria Visconti<br />
* Tesi di Francesco Cartella<br />
<br />
== Instruments ==<br />
<br />
* [[Electroencephalographs]]<br />
<br />
== How to ==<br />
<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]<br />
<br />
== Publications ==<br />
<br />
You can find publications in the BCI field by Airlab members on their home pages:<br />
* [http://home.dei.polimi.it/dalseno/publications.html Bernardo Dal Seno's publications]<br />
<br />
== Media ==<br />
<br />
* 22 Jan 2009: [http://tv.repubblica.it/copertina/muoversi-con-il-pensiero/28512?video Repubblica TV report on Lurch and BCI] (in Italian)<br />
* Aug 2008: [http://www.youtube.com/watch?v=lRP-ae4iaZA RAI TGLeonardo report on Airlab research] (in Italian). The video is a fragment of a longer report on mind and intelligence.</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9567Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T18:10:52Z<p>PaoloCalloni: </p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students ivolved in the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
Brain-computer interfaces (BCIs) make use of brain signals to transform specific patterns in subject's brain into control for an external device such as a wheelchair, a computer or a software application. Among the different technologies available to acquire the neural signal, the electroencephalography (EEG) has been chosen. At present, only EEG allows indeed to easily record and process the brain signal with inexpensive equipment, leading to a practical possibility of a new non-muscular and, mostly important, non-invasive communication channel. The most relevant drawback of using this type of approach is the strong presence of noise in the acquired signal appearing as the sum of potentials produced from various locations of the cortex and easily propagating through the head and being affected by muscular and ocular activity.<br />
<br />
Several paradigms have been developed in literature to exploit specific patterns in brain activity and to transform them into control signals for the considered device. In this thesis, the attention has been focused on the motor imagery (MI) and on the error potential (ErrP) paradigm.<br />
<br />
Motor imagery is a well studied technique of detecting changes in the electrical charge of cortical areas normally dedicated to the control of body movement. The imagination of specific body parts movement (e.g., a hand, a foot or the tongue) causes some precise areas of the motor cortex to change their electrical activity, increasing or decreasing their activity over some well defined frequency. The activities detectable at those frequency have been called rhythms. In particular, the so called \textitmu and beta rhythms which are located respectively in the bands 8-12 Hz and 15-22 Hz. By means of a spectral analysis over these bands the possibility to infer the imagined movement has been demonstrated. <br />
<br />
Event related potentials (ERPs) is a family of stimulus responses observable in acquired EEG signals as a common pattern happening just after some interesting, rare or unexpected event. Error potentials are a subset of ERPs generated by the awareness of making a mistake or by receiving unexpected results from the interface. In these situations a particular shape composed by a sequence of a negative potential and a subsequent positive one can be detected in the EEG recordings a few instants after receiving the stimulation. The detection of such a pattern can provide useful information to improve the overall performance of the BCI.<br />
<br />
The aim of this thesis is that of developing an interface able to provide both spectral and temporal analysis of the EEG signal to allow its classification into one of four possible classes (i.e., left, right, up, down) basing the analysis on the mentioned paradigms. The attention is focused on the individuation of a method capable to classify the user's motor imagery activity and, at the same time, to detect error potentials on the presented result to assess its validity.<br />
<br />
A method taking into account four MI classifications for each of the six seconds of a trial (i.e., a singular motor imagery recognition) and an equivalent number of ErrP detections has been developed. The idea is to provide a feedback after each second of the motor imagery activity, and to verify the possible presence of an error potential for each feedback presentation. The results collected from the two analysis are used to infer the most probable user motor intent. A Bayesian classifier employing data from training sessions is applied to produce the desired result. <br />
<br />
<br />
[[Image:Pipe.png]]<br />
<br />
<br />
Motor imagery classification proved to perform particularly well over a subject, achieving accuracies up to 75%. Other subjects achieved inferior but still interesting accuracies of 55%, 59% and 62%. The error potential detection reached an overall accuracy of 65% for one test subject. The proposed method to integrate the results of the two classifications processes lead to an increase in performance of 10\% in the subject achieving the 62% accuracy in the motor imagery paradigm. This result is encouraging as applied to a spelling interface leads to an increase of the spelling rate from 0.7 to 2.2 characters per minute.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
* Start studying material from course of Methodologies for Intelligent Systems<br />
* Start studying of ERP for applications in motor imagery <br />
* Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
* Analyzing ErrP caused by classification results in a MI task<br />
* Deploying a MI interface with visual synchronization signal<br />
* Interface with discrete feedback presentation ready for use<br />
* Interface with early classification feedback ready for testing<br />
* Started testing of newly created interfaces<br />
* Analizing P300+Errp code for merging<br />
* Brand new interface for better stimulation of potentials developed<br />
* Major improvement in file management for Matlab offline analysis<br />
* Set up of acquisition software for the new interface<br />
* Development of scripts to approximatively determine the end of training phase<br />
* Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Achieving a method for pipe branching in BCI2000<br />
* Beginning acquisition of data with ErrPs<br />
* Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs <br />
* Focusing on MIS to learn methods for smart use of error probability in class selection<br />
* Bayesian classifier developed to merge Motor Imagery and Error Potentials<br />
* Exhaustive mapping of solutions implemented for motor the imagery only case<br />
* Added error potentials to mapping<br />
* Tree organization of results and result explorer implemented<br />
* Testing online performances<br />
* Performing offline analysis<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 5: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9566Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T18:09:41Z<p>PaoloCalloni: /* Part 2: Project description */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students ivolved in the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
Brain-computer interfaces (BCIs) make use of brain signals to transform specific patterns in subject's brain into control for an external device such as a wheelchair, a computer or a software application. Among the different technologies available to acquire the neural signal, the electroencephalography (EEG) has been chosen. At present, only EEG allows indeed to easily record and process the brain signal with inexpensive equipment, leading to a practical possibility of a new non-muscular and, mostly important, non-invasive communication channel. The most relevant drawback of using this type of approach is the strong presence of noise in the acquired signal appearing as the sum of potentials produced from various locations of the cortex and easily propagating through the head and being affected by muscular and ocular activity.<br />
<br />
Several paradigms have been developed in literature to exploit specific patterns in brain activity and to transform them into control signals for the considered device. In this thesis, the attention has been focused on the motor imagery (MI) and on the error potential (ErrP) paradigm.<br />
<br />
Motor imagery is a well studied technique of detecting changes in the electrical charge of cortical areas normally dedicated to the control of body movement. The imagination of specific body parts movement (e.g., a hand, a foot or the tongue) causes some precise areas of the motor cortex to change their electrical activity, increasing or decreasing their activity over some well defined frequency. The activities detectable at those frequency have been called rhythms. In particular, the so called \textitmu and beta rhythms which are located respectively in the bands 8-12 Hz and 15-22 Hz. By means of a spectral analysis over these bands the possibility to infer the imagined movement has been demonstrated. <br />
<br />
Event related potentials (ERPs) is a family of stimulus responses observable in acquired EEG signals as a common pattern happening just after some interesting, rare or unexpected event. Error potentials are a subset of ERPs generated by the awareness of making a mistake or by receiving unexpected results from the interface. In these situations a particular shape composed by a sequence of a negative potential and a subsequent positive one can be detected in the EEG recordings a few instants after receiving the stimulation. The detection of such a pattern can provide useful information to improve the overall performance of the BCI.<br />
<br />
The aim of this thesis is that of developing an interface able to provide both spectral and temporal analysis of the EEG signal to allow its classification into one of four possible classes (i.e., left, right, up, down) basing the analysis on the mentioned paradigms. The attention is focused on the individuation of a method capable to classify the user's motor imagery activity and, at the same time, to detect error potentials on the presented result to assess its validity.<br />
<br />
A method taking into account four MI classifications for each of the six seconds of a trial (i.e., a singular motor imagery recognition) and an equivalent number of ErrP detections has been developed. The idea is to provide a feedback after each second of the motor imagery activity, and to verify the possible presence of an error potential for each feedback presentation. The results collected from the two analysis are used to infer the most probable user motor intent. A Bayesian classifier employing data from training sessions is applied to produce the desired result. <br />
<br />
<br />
[[Image:Pipe.png]]<br />
<br />
<br />
Motor imagery classification proved to perform particularly well over a subject, achieving accuracies up to 75%. Other subjects achieved inferior but still interesting accuracies of 55%, 59% and 62%. The error potential detection reached an overall accuracy of 65% for one test subject. The proposed method to integrate the results of the two classifications processes lead to an increase in performance of 10\% in the subject achieving the 62% accuracy in the motor imagery paradigm. This result is encouraging as applied to a spelling interface leads to an increase of the spelling rate from 0.7 to 2.2 characters per minute.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
* Start studying material from course of Methodologies for Intelligent Systems<br />
* Start studying of ERP for applications in motor imagery <br />
* Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
* Analyzing ErrP caused by classification results in a MI task<br />
* Deploying a MI interface with visual synchronization signal<br />
* Interface with discrete feedback presentation ready for use<br />
* Interface with early classification feedback ready for testing<br />
* Started testing of newly created interfaces<br />
* Analizing P300+Errp code for merging<br />
* Brand new interface for better stimulation of potentials developed<br />
* Major improvement in file management for Matlab offline analysis<br />
* Set up of acquisition software for the new interface<br />
* Development of scripts to approximatively determine the end of training phase<br />
* Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Achieving a method for pipe branching in BCI2000<br />
* Beginning acquisition of data with ErrPs<br />
* Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs <br />
* Focusing on MIS to learn methods for smart use of error probability in class selection<br />
* Bayesian classifier developed to merge Motor Imagery and Error Potentials<br />
* Exhaustive mapping of solutions implemented for motor the imagery only case<br />
* Added error potentials to mapping<br />
* Tree organization of results and result explorer implemented<br />
* Testing online performances<br />
* Performing offline analysis<br />
<br />
== '''Part 5: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 6: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=File:Pipe.png&diff=9565File:Pipe.png2009-12-07T18:08:06Z<p>PaoloCalloni: Online motor imagery and error potential processing pipe</p>
<hr />
<div>Online motor imagery and error potential processing pipe</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9564Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T18:01:14Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students ivolved in the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
Brain-computer interfaces (BCIs) make use of brain signals to transform specific patterns in subject's brain into control for an external device such as a wheelchair, a computer or a software application. Among the different technologies available to acquire the neural signal, the electroencephalography (EEG) has been chosen. At present, only EEG allows indeed to easily record and process the brain signal with inexpensive equipment, leading to a practical possibility of a new non-muscular and, mostly important, non-invasive communication channel. The most relevant drawback of using this type of approach is the strong presence of noise in the acquired signal appearing as the sum of potentials produced from various locations of the cortex and easily propagating through the head and being affected by muscular and ocular activity.<br />
<br />
Several paradigms have been developed in literature to exploit specific patterns in brain activity and to transform them into control signals for the considered device. In this thesis, the attention has been focused on the motor imagery (MI) and on the error potential (ErrP) paradigm.<br />
<br />
Motor imagery is a well studied technique of detecting changes in the electrical charge of cortical areas normally dedicated to the control of body movement. The imagination of specific body parts movement (e.g., a hand, a foot or the tongue) causes some precise areas of the motor cortex to change their electrical activity, increasing or decreasing their activity over some well defined frequency. The activities detectable at those frequency have been called rhythms. In particular, the so called \textitmu and beta rhythms which are located respectively in the bands 8-12 Hz and 15-22 Hz. By means of a spectral analysis over these bands the possibility to infer the imagined movement has been demonstrated. <br />
<br />
Event related potentials (ERPs) is a family of stimulus responses observable in acquired EEG signals as a common pattern happening just after some interesting, rare or unexpected event. Error potentials are a subset of ERPs generated by the awareness of making a mistake or by receiving unexpected results from the interface. In these situations a particular shape composed by a sequence of a negative potential and a subsequent positive one can be detected in the EEG recordings a few instants after receiving the stimulation. The detection of such a pattern can provide useful information to improve the overall performance of the BCI.<br />
<br />
The aim of this thesis is that of developing an interface able to provide both spectral and temporal analysis of the EEG signal to allow its classification into one of four possible classes (i.e., left, right, up, down) basing the analysis on the mentioned paradigms. The attention is focused on the individuation of a method capable to classify the user's motor imagery activity and, at the same time, to detect error potentials on the presented result to assess its validity.<br />
<br />
A method taking into account four MI classifications for each of the six seconds of a trial (i.e., a singular motor imagery recognition) and an equivalent number of ErrP detections has been developed. The idea is to provide a feedback after each second of the motor imagery activity, and to verify the possible presence of an error potential for each feedback presentation. The results collected from the two analysis are used to infer the most probable user motor intent. A Bayesian classifier employing data from training sessions is applied to produce the desired result. <br />
<br />
Motor imagery classification proved to perform particularly well over a subject, achieving accuracies up to 75%. Other subjects achieved inferior but still interesting accuracies of 55%, 59% and 62%. The error potential detection reached an overall accuracy of 65% for one test subject. The proposed method to integrate the results of the two classifications processes lead to an increase in performance of 10\% in the subject achieving the 62% accuracy in the motor imagery paradigm. This result is encouraging as applied to a spelling interface leads to an increase of the spelling rate from 0.7 to 2.2 characters per minute.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
* Start studying material from course of Methodologies for Intelligent Systems<br />
* Start studying of ERP for applications in motor imagery <br />
* Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
* Analyzing ErrP caused by classification results in a MI task<br />
* Deploying a MI interface with visual synchronization signal<br />
* Interface with discrete feedback presentation ready for use<br />
* Interface with early classification feedback ready for testing<br />
* Started testing of newly created interfaces<br />
* Analizing P300+Errp code for merging<br />
* Brand new interface for better stimulation of potentials developed<br />
* Major improvement in file management for Matlab offline analysis<br />
* Set up of acquisition software for the new interface<br />
* Development of scripts to approximatively determine the end of training phase<br />
* Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Achieving a method for pipe branching in BCI2000<br />
* Beginning acquisition of data with ErrPs<br />
* Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs <br />
* Focusing on MIS to learn methods for smart use of error probability in class selection<br />
* Bayesian classifier developed to merge Motor Imagery and Error Potentials<br />
* Exhaustive mapping of solutions implemented for motor the imagery only case<br />
* Added error potentials to mapping<br />
* Tree organization of results and result explorer implemented<br />
* Testing online performances<br />
* Performing offline analysis<br />
<br />
== '''Part 5: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 6: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9563Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T14:06:28Z<p>PaoloCalloni: /* Students currently working on the project */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students ivolved in the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
Brain-computer interfaces (BCIs) make use of brain signals to transform specific patterns in subject's brain into control for an external device such as a wheelchair, a computer or a software application. Among the different technologies available to acquire the neural signal, the electroencephalography (EEG) has been chosen. At present, only EEG allows indeed to easily record and process the brain signal with inexpensive equipment, leading to a practical possibility of a new non-muscular and, mostly important, non-invasive communication channel. The most relevant drawback of using this type of approach is the strong presence of noise in the acquired signal appearing as the sum of potentials produced from various locations of the cortex and easily propagating through the head and being affected by muscular and ocular activity.<br />
<br />
Several paradigms have been developed in literature to exploit specific patterns in brain activity and to transform them into control signals for the considered device. In this thesis, the attention has been focused on the motor imagery (MI) and on the error potential (ErrP) paradigm.<br />
<br />
Motor imagery is a well studied technique of detecting changes in the electrical charge of cortical areas normally dedicated to the control of body movement. The imagination of specific body parts movement (e.g., a hand, a foot or the tongue) causes some precise areas of the motor cortex to change their electrical activity, increasing or decreasing their activity over some well defined frequency. The activities detectable at those frequency have been called rhythms. In particular, the so called \textitmu and beta rhythms which are located respectively in the bands 8-12 Hz and 15-22 Hz. By means of a spectral analysis over these bands the possibility to infer the imagined movement has been demonstrated. <br />
<br />
Event related potentials (ERPs) is a family of stimulus responses observable in acquired EEG signals as a common pattern happening just after some interesting, rare or unexpected event. Error potentials are a subset of ERPs generated by the awareness of making a mistake or by receiving unexpected results from the interface. In these situations a particular shape composed by a sequence of a negative potential and a subsequent positive one can be detected in the EEG recordings a few instants after receiving the stimulation. The detection of such a pattern can provide useful information to improve the overall performance of the BCI.<br />
<br />
The aim of this thesis is that of developing an interface able to provide both spectral and temporal analysis of the EEG signal to allow its classification into one of four possible classes (i.e., left, right, up, down) basing the analysis on the mentioned paradigms. The attention is focused on the individuation of a method capable to classify the user's motor imagery activity and, at the same time, to detect error potentials on the presented result to assess its validity.<br />
<br />
A method taking into account four MI classifications for each of the six seconds of a trial (i.e., a singular motor imagery recognition) and an equivalent number of ErrP detections has been developed. The idea is to provide a feedback after each second of the motor imagery activity, and to verify the possible presence of an error potential for each feedback presentation. The results collected from the two analysis are used to infer the most probable user motor intent. A Bayesian classifier employing data from training sessions is applied to produce the desired result. <br />
<br />
Motor imagery classification proved to perform particularly well over a subject, achieving accuracies up to 75%. Other subjects achieved inferior but still interesting accuracies of 55%, 59% and 62%. The error potential detection reached an overall accuracy of 65% for one test subject. The proposed method to integrate the results of the two classifications processes lead to an increase in performance of 10\% in the subject achieving the 62% accuracy in the motor imagery paradigm. This result is encouraging as applied to a spelling interface leads to an increase of the spelling rate from 0.7 to 2.2 characters per minute.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
<br />
<br />
== '''Part 5: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 6: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9562Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T14:05:45Z<p>PaoloCalloni: /* Part 2: Project description */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
Brain-computer interfaces (BCIs) make use of brain signals to transform specific patterns in subject's brain into control for an external device such as a wheelchair, a computer or a software application. Among the different technologies available to acquire the neural signal, the electroencephalography (EEG) has been chosen. At present, only EEG allows indeed to easily record and process the brain signal with inexpensive equipment, leading to a practical possibility of a new non-muscular and, mostly important, non-invasive communication channel. The most relevant drawback of using this type of approach is the strong presence of noise in the acquired signal appearing as the sum of potentials produced from various locations of the cortex and easily propagating through the head and being affected by muscular and ocular activity.<br />
<br />
Several paradigms have been developed in literature to exploit specific patterns in brain activity and to transform them into control signals for the considered device. In this thesis, the attention has been focused on the motor imagery (MI) and on the error potential (ErrP) paradigm.<br />
<br />
Motor imagery is a well studied technique of detecting changes in the electrical charge of cortical areas normally dedicated to the control of body movement. The imagination of specific body parts movement (e.g., a hand, a foot or the tongue) causes some precise areas of the motor cortex to change their electrical activity, increasing or decreasing their activity over some well defined frequency. The activities detectable at those frequency have been called rhythms. In particular, the so called \textitmu and beta rhythms which are located respectively in the bands 8-12 Hz and 15-22 Hz. By means of a spectral analysis over these bands the possibility to infer the imagined movement has been demonstrated. <br />
<br />
Event related potentials (ERPs) is a family of stimulus responses observable in acquired EEG signals as a common pattern happening just after some interesting, rare or unexpected event. Error potentials are a subset of ERPs generated by the awareness of making a mistake or by receiving unexpected results from the interface. In these situations a particular shape composed by a sequence of a negative potential and a subsequent positive one can be detected in the EEG recordings a few instants after receiving the stimulation. The detection of such a pattern can provide useful information to improve the overall performance of the BCI.<br />
<br />
The aim of this thesis is that of developing an interface able to provide both spectral and temporal analysis of the EEG signal to allow its classification into one of four possible classes (i.e., left, right, up, down) basing the analysis on the mentioned paradigms. The attention is focused on the individuation of a method capable to classify the user's motor imagery activity and, at the same time, to detect error potentials on the presented result to assess its validity.<br />
<br />
A method taking into account four MI classifications for each of the six seconds of a trial (i.e., a singular motor imagery recognition) and an equivalent number of ErrP detections has been developed. The idea is to provide a feedback after each second of the motor imagery activity, and to verify the possible presence of an error potential for each feedback presentation. The results collected from the two analysis are used to infer the most probable user motor intent. A Bayesian classifier employing data from training sessions is applied to produce the desired result. <br />
<br />
Motor imagery classification proved to perform particularly well over a subject, achieving accuracies up to 75%. Other subjects achieved inferior but still interesting accuracies of 55%, 59% and 62%. The error potential detection reached an overall accuracy of 65% for one test subject. The proposed method to integrate the results of the two classifications processes lead to an increase in performance of 10\% in the subject achieving the 62% accuracy in the motor imagery paradigm. This result is encouraging as applied to a spelling interface leads to an increase of the spelling rate from 0.7 to 2.2 characters per minute.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
<br />
<br />
== '''Part 5: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 6: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9561Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T14:04:59Z<p>PaoloCalloni: /* Part 2: Project description */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
Communication with surrounding world is usually precluded to people suffering from severe motor disorders or that are completely paralyzed. This condition always reflects upon a state of depression and heavily affects the already compromised quality of life of the affected subjects. Currently available augmentative communication techniques are, for the largest part, methods requiring residual muscle control to operate. They make use of one muscle group to supply the function normally provided by others (e.g., use extraocular muscles to drive a speech synthesizer) or detour around interruptions in normal pathways (e.g., use shoulder muscles to control activation of hand and forearm muscles), hence they require some measure of voluntary muscle function.<br />
<br />
Unfortunately, cases of severe motor diseases in which there is no residual voluntary control of muscles exist. This is the case of subjects affected by total paralysis, caused by neuro-degenerative disorders, like in the case of amyotrophic lateral sclerosis (ALS), by brain-stem stroke or suffering from other severe motor disabilities. In this thesis, the attention is paid to those individuals affected by the so called locked-in syndrome, responsible of the total absence of voluntary muscular activity. It has been proved that also in people affected by this syndrome, which leaves the subject cognitive capabilities unaltered, the neuronal activity of some cortical area can lead to classification of subject intent in specific tasks.<br />
<br />
These individuals need an alternative communication channel that does not depend on muscle control. They need a method to express their wishes and that does not rely on the brain normal output pathways of peripheral nerves and muscles. <br />
<br />
Brain-computer interfaces (BCIs) make use of brain signals to transform specific patterns in subject's brain into control for an external device such as a wheelchair, a computer or a software application. Among the different technologies available to acquire the neural signal, the electroencephalography (EEG) has been chosen. At present, only EEG allows indeed to easily record and process the brain signal with inexpensive equipment, leading to a practical possibility of a new non-muscular and, mostly important, non-invasive communication channel. The most relevant drawback of using this type of approach is the strong presence of noise in the acquired signal appearing as the sum of potentials produced from various locations of the cortex and easily propagating through the head and being affected by muscular and ocular activity.<br />
<br />
Several paradigms have been developed in literature to exploit specific patterns in brain activity and to transform them into control signals for the considered device. In this thesis, the attention has been focused on the motor imagery (MI) and on the error potential (ErrP) paradigm.<br />
<br />
Motor imagery is a well studied technique of detecting changes in the electrical charge of cortical areas normally dedicated to the control of body movement. The imagination of specific body parts movement (e.g., a hand, a foot or the tongue) causes some precise areas of the motor cortex to change their electrical activity, increasing or decreasing their activity over some well defined frequency. The activities detectable at those frequency have been called rhythms. In particular, the so called \textitmu and beta rhythms which are located respectively in the bands 8-12 Hz and 15-22 Hz. By means of a spectral analysis over these bands the possibility to infer the imagined movement has been demonstrated. <br />
<br />
Event related potentials (ERPs) is a family of stimulus responses observable in acquired EEG signals as a common pattern happening just after some interesting, rare or unexpected event. Error potentials are a subset of ERPs generated by the awareness of making a mistake or by receiving unexpected results from the interface. In these situations a particular shape composed by a sequence of a negative potential and a subsequent positive one can be detected in the EEG recordings a few instants after receiving the stimulation. The detection of such a pattern can provide useful information to improve the overall performance of the BCI.<br />
<br />
The aim of this thesis is that of developing an interface able to provide both spectral and temporal analysis of the EEG signal to allow its classification into one of four possible classes (i.e., left, right, up, down) basing the analysis on the mentioned paradigms. The attention is focused on the individuation of a method capable to classify the user's motor imagery activity and, at the same time, to detect error potentials on the presented result to assess its validity.<br />
<br />
A method taking into account four MI classifications for each of the six seconds of a trial (i.e., a singular motor imagery recognition) and an equivalent number of ErrP detections has been developed. The idea is to provide a feedback after each second of the motor imagery activity, and to verify the possible presence of an error potential for each feedback presentation. The results collected from the two analysis are used to infer the most probable user motor intent. A Bayesian classifier employing data from training sessions is applied to produce the desired result. <br />
<br />
Motor imagery classification proved to perform particularly well over a subject, achieving accuracies up to 75%. Other subjects achieved inferior but still interesting accuracies of 55%, 59% and 62%. The error potential detection reached an overall accuracy of 65% for one test subject. The proposed method to integrate the results of the two classifications processes lead to an increase in performance of 10\% in the subject achieving the 62% accuracy in the motor imagery paradigm. This result is encouraging as applied to a spelling interface leads to an increase of the spelling rate from 0.7 to 2.2 characters per minute.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
<br />
<br />
== '''Part 5: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 6: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9560Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T12:25:41Z<p>PaoloCalloni: /* Part 4: Documents */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
<br />
<br />
== '''Part 5: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 6: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9559Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T12:25:06Z<p>PaoloCalloni: /* Dates */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
== '''Part 4: Documents''' ==<br />
<br />
* Thesis document: [http://www.slideshare.net/tizyweb/a-predictive-speller-for-a-braincomputer-interface-based-on-motorimagery-1752428 A predictive speller for a brain-computer interface based on motor imagery] [En]<br />
* Presentation [http://www.slideshare.net/tizyweb/a-predictive-speller-for-a-braincomputer-interface-based-on-motorimagery A predictive speller for a brain-computer interface based on motor imagery (presentation)][En]<br />
* Video [http://www.youtube.com/watch?v=R-tNE-y2QU0 A predictive speller for a brain-computer interface based on motor imagery (video)]<br />
<br />
== '''Part 5: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 6: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Integrating_Motor_Imagery_and_Error_Potentials_in_a_Brain-Computer_Interface&diff=9558Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface2009-12-07T12:24:53Z<p>PaoloCalloni: New page: == '''Part 1: Project profile''' == === Project name === Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study === Project sh...</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface based on Motor Imagery: A Case Study <br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
<br />
Start date: 01/12/2008<br />
End date: 01/12/2009<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:PaoloCalloni | Paolo Calloni]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
== '''Part 4: Documents''' ==<br />
<br />
* Thesis document: [http://www.slideshare.net/tizyweb/a-predictive-speller-for-a-braincomputer-interface-based-on-motorimagery-1752428 A predictive speller for a brain-computer interface based on motor imagery] [En]<br />
* Presentation [http://www.slideshare.net/tizyweb/a-predictive-speller-for-a-braincomputer-interface-based-on-motorimagery A predictive speller for a brain-computer interface based on motor imagery (presentation)][En]<br />
* Video [http://www.youtube.com/watch?v=R-tNE-y2QU0 A predictive speller for a brain-computer interface based on motor imagery (video)]<br />
<br />
== '''Part 5: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== '''Part 6: Links''' ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9557BCI based on Motor Imagery2009-12-07T12:18:55Z<p>PaoloCalloni: </p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
== '''Website(s)''' ==<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
[http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki]<br />
<br />
== '''Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms.<br />
<br />
<br />
<br />
== '''References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
== '''Links''' ==<br />
<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]<br />
<br />
== '''External Links''' ==<br />
<br />
* [http://www.bci2000.org/BCI2000/Home.html BCI2000 Website]<br />
* [http://www.bci2000.org/wiki/index.php/Main_Page BCI2000 Wiki]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9556BCI based on Motor Imagery2009-12-07T12:16:03Z<p>PaoloCalloni: /* Links */</p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
== '''Website(s)''' ==<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
[http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki]<br />
<br />
== '''Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms.<br />
<br />
<br />
<br />
== '''References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
== '''Links''' ==<br />
<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9555BCI based on Motor Imagery2009-12-07T12:15:16Z<p>PaoloCalloni: /* Part 4: References */</p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
== '''Website(s)''' ==<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
[http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki]<br />
<br />
== '''Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms.<br />
<br />
<br />
<br />
== '''References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9554BCI based on Motor Imagery2009-12-07T12:14:53Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
== '''Website(s)''' ==<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
[http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki]<br />
<br />
== '''Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms.<br />
<br />
<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9553BCI based on Motor Imagery2009-12-07T12:12:34Z<p>PaoloCalloni: /* Website(s) */</p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
== '''Website(s)''' ==<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
[http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki]<br />
<br />
== '''Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9552BCI based on Motor Imagery2009-12-07T12:11:48Z<p>PaoloCalloni: /* Website(s) */</p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
=== '''Website(s)''' ===<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
[http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki]<br />
<br />
== '''Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9551BCI based on Motor Imagery2009-12-07T12:11:25Z<p>PaoloCalloni: /* Part 2: Project description */</p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
=== Website(s) ===<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
[http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki]<br />
<br />
== '''Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms.<br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9550BCI based on Motor Imagery2009-12-07T12:10:51Z<p>PaoloCalloni: /* Website(s) */</p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
=== Website(s) ===<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
[http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki]<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=9549BCI based on Motor Imagery2009-12-07T12:05:23Z<p>PaoloCalloni: /* Website(s) */</p>
<hr />
<div>{{Project<br />
|title=BCI based on Motor Imagery<br />
|short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
|tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno<br />
|students=FabioZennaro<br />
|resarea=BioSignal Analysis<br />
|status=Active<br />
}}<br />
=== Website(s) ===<br />
<br />
[http://airlab.ws.dei.polimi.it/index.php?option=com_content&view=article&id=7:biosignal-analysis&catid=3:research-areas&Itemid=5 BioSignal Analysis on Airlab website]<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Brain-Computer_Interface&diff=9548Brain-Computer Interface2009-12-07T11:55:27Z<p>PaoloCalloni: /* Ongoing projects */</p>
<hr />
<div>A Brain-Computer Interface (BCI) is an experimental communication system that allows an individual to control a device by using signals from the brain (e.g., electroencephalography -- EEG).<br />
<br />
You can find a longer description on the [http://airlab.elet.polimi.it/index.php/airlab/research_areas/biosignal_analysis?z=2299 AIRLab page].<br />
<br />
The BCI project is in the [[BioSignal_Analysis]] area.<br />
<br />
== Ongoing projects ==<br />
<br />
* [[A genetic algorithm for automatic feature extraction from EEG data]]<br />
* [[BCI based on Motor Imagery]]<br />
** [[Predictive BCI Speller based on Motor Imagery]] (Master thesis, Tiziano D'Albis)<br />
** [[Feature Selection and Extraction for a BCI based on motor imagery]] (Master thesis, Francesco Amenta)<br />
** [[Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface]] (Master Thesis, Paolo Calloni)<br />
** [[Ocular Artifacts Filter implementation for a BCI based on motor imagery]] (First Level thesis, Fabio Beltramini)<br />
* [[Graphical user interface for an autonomous wheelchair]]<br />
* [[Online automatic tuning of the number of repetitions in a P300-based BCI]]<br />
<br />
== New projects ==<br />
There are various proposal for students interested in projects/thesis in the field of brain-computer interfaces:<br />
*[[First Level Course Projects#Brain-Computer_Interface|First Level Course Projects]]<br />
*[[First Level Theses#Brain-Computer_Interface|First Level Theses]]<br />
*[[Master Level Course Projects#Brain-Computer_Interface|Master Level Course Projects]]<br />
*[[Master Level Theses#Brain-Computer_Interface|Master Level Theses]]<br />
<br />
== Finished projects ==<br />
<br />
* [[Reproduction of an algorithm for the recognition of error potentials]]<br />
* [[Online P300 and ErrP recognition with BCI2000]] (Master thesis, Andrea Sgarlata).<br />
* Tesi di Carlo Gimondi e Luisella Messana <br />
* Tesi di Gianmaria Visconti<br />
* Tesi di Francesco Cartella<br />
<br />
== Instruments ==<br />
<br />
* [[Electroencephalographs]]<br />
<br />
== How to ==<br />
<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]<br />
<br />
== Publications ==<br />
<br />
You can find publications in the BCI field by Airlab members on their home pages:<br />
* [http://home.dei.polimi.it/dalseno/publications.html Bernardo Dal Seno's publications]<br />
<br />
== Media ==<br />
<br />
* 22 Jan 2009: [http://tv.repubblica.it/copertina/muoversi-con-il-pensiero/28512?video Repubblica TV report on Lurch and BCI] (in Italian)<br />
* Aug 2008: [http://www.youtube.com/watch?v=lRP-ae4iaZA RAI TGLeonardo report on Airlab research] (in Italian). The video is a fragment of a longer report on mind and intelligence.</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Projects&diff=9547Projects2009-12-07T11:54:04Z<p>PaoloCalloni: /* Brain-Computer Interface */</p>
<hr />
<div>''This page is a repository of links to the pages describing the '''projects''' we are currently working on at AIRLab. <br />
See the list of our finished projects on the [[Finished Projects]] page.''<br />
<br />
== Ongoing projects ==<br />
''by research area (areas are defined in the [[Main Page]]); for each project a name and a link to its AIRWiki page is given''<br />
<br />
=== [[Agents, Multiagent Systems, Agencies]] ===<br />
----<br />
===== [[Strategic Robot Patrolling]] =====<br />
<br />
===== [[Evolutionary game theory for biology]] =====<br />
===== [[Game theoretic analysis of electric power]] =====<br />
===== [[Algorithms for computing equilibria]] =====<br />
===== [[Multiagent cooperation|Multiagent cooperating system]] =====<br />
===== [[Planning in Ambient Intelligence scenarios]] =====<br />
===== [[Real-Time Strategy Games]] =====<br />
<br />
=== [[BioSignal Analysis]] ===<br />
----<br />
===== [[Affective Computing]] =====<br />
* [[Relatioship between Cognition and Emotion in Rehabilitation Robotics]]<br />
* [[Driving companions]]<br />
* [[Emotion from Interaction]]<br />
* [[Wireless Affective Devices]]<br />
* [[Affective Robot force sensor]]<br />
* [[Affective VideoGames]]<br />
<br />
===== [[Brain-Computer Interface]] =====<br />
* [[BCI based on Motor Imagery]]<br />
** [[Predictive BCI Speller based on Motor Imagery]] (Master thesis, Tiziano D'Albis)<br />
** [[Feature Selection and Extraction for a BCI based on motor imagery]] (Master thesis, Francesco Amenta)<br />
** [[Integrating Motor Imagery and Error Potentials in a Brain-Computer Interface]] (Master Thesis, Paolo Calloni)<br />
** [[Ocular Artifacts Filter implementation for a BCI based on motor imagery]] (First Level thesis, Fabio Beltramini)<br />
* [[Online automatic tuning of the number of repetitions in a P300-based BCI]] (First Level thesis, Siegfried Cattaneo)<br />
* [[Graphical user interface for an autonomous wheelchair]] (First Level thesis, Antonio Tripodi and Eleonora Ciceri)<br />
* [[Mu and beta rhythm-based BCI]]<br />
* [[Reproduction of an algorithm for the recognition of error potentials]]<br />
* [[Stimulus tagging using aperiodic visual stimulation in a VEP-based BCI]]<br />
<br />
===== [[Automatic Detection Of Sleep Stages]] =====<br />
* [[Sleep Staging with HMM]]<br />
<br />
===== [[Analysis of the Olfactory Signal]] =====<br />
* [[Lung Cancer Detection by an Electronic Nose]]<br />
* [[HE-KNOWS - An electronic nose]]<br />
<br />
===== [[Classification of EMG signals]] =====<br />
<br />
=== [[Computer Vision and Image Analysis]] ===<br />
----<br />
* [[Automated extraction of laser streaks and range profiles]]<br />
* [[Data collection for mutual calibration|Data collection for laser-rangefinder and camera calibration]]<br />
* [[Image retargeting by k-seam removal]]<br />
* [[Particle filter for object tracking]]<br />
* [[Template based paper like reconstruction when the edges are straight]]<br />
* [[Wii Remote headtracking and active projector]]<br />
* [[Vision module for the Milan Robocup Team]]<br />
* [[Long Exposure Images for Resource-constrained video surveillance]]<br />
* [[NonPhotorealistic rendering of speed lines]].<br />
* [[Restoration of blurred objects using cues from the alpha matte]]<br />
* [[Analyzing Traffic Speed From a Single Night Image - Light Streaks Detection]]<br />
* [[Plate detection algorithm]]<br />
* [[A vision-based 3D input device for space curves]]<br />
* [[Correlation-based 3D reconstruction with pan/tilt stereo-camera]]<br />
* [[Inverse scaling parametrization for Monocular Simultaneous Localization and Mapping]]<br />
* [[Image resize by solving a sparse linear system]]<br />
* [[Monocular Simultaneous Localization And Mapping with Moving Object Tracking using Conditional Independent submaps]]<br />
* [[Robust data association for high-speed SLAM]]<br />
* [[Automated Recognition between alkaline and non-alkaline AA batteries]]<br />
* [[Hand gesture guided desktop lamp]]<br />
<br />
=== [[Machine Learning]] ===<br />
----<br />
* [[Adaptive Reinforcement Learning Multiagent Coordination in Real-Time Computer Games|Adaptive Reinforcement Learning Multiagent Coordination in Real-Time Computer Games]]<br />
* [[B-Smart Behaviour Sequence Modeler and Recognition tool|B-Smart Behaviour Sequence Modeler and Recognition tool]]<br />
* [[Giskar - Distance estimation through single camera features applied to Neural Networks]]<br />
* [[Exploit of betting patterns using genetic algorithms and reinforcement learning]]<br />
<br />
* [[Q_Fitted_Algorithm:_The_Dam_Problem]]<br />
<br />
=== [[Evolutionary Computation]] ===<br />
----<br />
<br />
* [[Evoptool: Evolutive Optimization Tool]]<br />
<br />
=== [[Philosophy of Artificial Intelligence]] ===<br />
----<br />
<br />
=== [[Robotics]] ===<br />
----<br />
<br />
==== [[Robot development]] ====<br />
* [[ExhiBot - A robot for exhibitions]]<br />
* [[LURCH - The autonomous wheelchair]]<br />
* [[Balancing robots: Tilty, TiltOne]]<br />
* [[Robotizing a Golf Cart ]]<br />
* [[ Development of a neck for humanoid robot ]]<br />
* [[Development of robot Maximum One - control and programming ]]<br />
* [[Milan Robocup Team Robot development ]]<br />
* [[Modular Robotic Toolkit ]]<br />
* [[Indoor localization system based on a gyro and visual passive markers]]<br />
* [[Simulation of a 6 DOF Manipulator]]<br />
<br />
==== [[Benchmarking]] ====<br />
* [[Rawseeds|RAWSEEDS]]<br />
<br />
==== [[Bio Robotics]] ====<br />
* [[PoliManus]]<br />
* [[ZOIDBERG - An autonomous bio-inspired RoboFish]]<br />
* [[Styx The 6 Whegs Robot]]<br />
* [[PolyGlove: a body-based haptic interface]]<br />
* [[ULISSE]]<br />
* [[PEKeB: a PiezoElectric KeyBoard]]<br />
* [[Anthropomorphic Robotic Wrist]]<br />
* [[High-level architecture for the control of humanoid robot]]<br />
* [[Zoidberg II, powering robot fish]]<br />
* [[EMG, new test]]<br />
* [[CPG for Warugadar]]<br />
* [[Hand prosthesis using robotics principles]]<br />
* [[Control of Whitefinger]]<br />
<br />
==== [[Robogames]] ====<br />
* [[ROBOWII]]<br />
* [[RobogameDesign]]<br />
<br />
==== [[Navigation Strategies]] ====<br />
* [[ Navigation system for LURCH ]]<br />
<br />
=== [[Social Software and Semantic Web]] ===<br />
----<br />
===== [[Social Network Analysis| Extracting Knowledge From Social Networks]] =====<br />
<br />
{{#ask: [[Category:Project]][[prjResTopic::Social Network Analysis]][[prjStatus::Active]]|?prjTitle = |format=ul}} <br />
<br />
===== [[Semantic Tagging]] =====<br />
<br />
{{#ask: [[Category:Project]][[prjResTopic::Semantic Tagging]][[prjStatus::Active]]|?prjTitle = |format=ul}}<br />
<br />
===== [[Semantic Search]] =====<br />
<br />
{{#ask: [[Category:Project]][[prjResTopic::Semantic Search]][[prjStatus::Active]]|?prjTitle = |format=ul}}<br />
<br />
<!-- <br />
===== [[Semantic Annotations]] =====<br />
<br />
{{#ask: [[Category:Project]][[prjResTopic::Semantic Annotations]][[prjStatus::Active]]|?prjTitle = |sort=prjEnd|order=desc|format=ul}}<br />
--><br />
<br />
== Past projects ==<br />
<br />
=== [[BioSignal Analysis]] ===<br />
<br />
===== [[Affective Computing]] =====<br />
<br />
{{#ask: [[Category:Project]][[prjResTopic::Affective Computing]] [[PrjEnd::<{{CURRENTYEAR}}/{{CURRENTMONTH}}/{{CURRENTDAY}}]]|format=ul}}<br />
<br />
===== [[Brain-Computer Interface]] =====<br />
* [[Online P300 and ErrP recognition with BCI2000]]<br />
<br />
=== [[Robotics]] ===<br />
*[[2D Mapping Using a Quadtree Data Structure]]<br />
<br />
== April Fool's projects ==<br />
<br />
Following the [http://en.wikipedia.org/wiki/April_Fools%27_Day_RFC RFC] tradition,<br />
[[April_1st_Projects|here]] is our April Fool's project page.<br />
<br />
== Note for students == <br />
<br />
If you are a student and there isn't a '''page describing your project''', this is because YOU have the task of creating it and populating it with (meaningful) content. If you are a student and there IS a page describing your project, you have the task to complete that page with (useful and comprehensive) information about your own contribution to the project. Be aware that the quality of your work (or lack of it) on the AIRWiki will be evaluated by the Teachers and will influence your grades.<br />
<br />
Instructions to add a new project or to add content to an existing project page are available at [[Projects - HOWTO]].</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7673BCI based on Motor Imagery2009-09-07T11:54:05Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/12/2008<br />
<br />
End date:<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required) <br />
Week 5:<br />
** Focusing on MIS to learn methods for smart use of error probability in class selection<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7655BCI based on Motor Imagery2009-09-03T09:06:31Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/12/2008<br />
<br />
End date:<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Start studying material from course of Methodologies for Intelligent Systems<br />
** Start studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzing ErrP caused by classification results in a MI task<br />
** Deploying a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for testing<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7654BCI based on Motor Imagery2009-09-03T09:04:39Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/12/2008<br />
<br />
End date:<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Started studying material from course of Methodologies for Intelligent Systems<br />
** Started studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzed ErrP caused by classification results in a MI task<br />
** Deployed a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for use<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
** Beginning acquisition of data with ErrPs<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7645BCI based on Motor Imagery2009-08-31T19:57:16Z<p>PaoloCalloni: /* Dates */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/12/2008<br />
<br />
End date:<br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Started studying material from course of Methodologies for Intelligent Systems<br />
** Started studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzed ErrP caused by classification results in a MI task<br />
** Deployed a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for use<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7644BCI based on Motor Imagery2009-08-31T19:55:51Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/05/2008<br />
<br />
End date: <br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Started studying material from course of Methodologies for Intelligent Systems<br />
** Started studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzed ErrP caused by classification results in a MI task<br />
** Deployed a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for use<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
* Week 4:<br />
** Achieving a method for pipe branching in BCI2000<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7640BCI based on Motor Imagery2009-08-29T17:49:38Z<p>PaoloCalloni: /* Students currently working on the project */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/05/2008<br />
<br />
End date: <br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Started studying material from course of Methodologies for Intelligent Systems<br />
** Started studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzed ErrP caused by classification results in a MI task<br />
** Deployed a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for use<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7639BCI based on Motor Imagery2009-08-29T17:48:30Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/05/2008<br />
<br />
End date: <br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:TizianoDalbis | Tiziano D'Albis]] (master student)<br />
* [[User:FabioBeltramini | Fabio Beltramini]] (bachelor student)<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Started studying material from course of Methodologies for Intelligent Systems<br />
** Started studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzed ErrP caused by classification results in a MI task<br />
** Deployed a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for use<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase (discarded)<br />
** Merging and adapting code of P300+ErrP to motor imagery tasks<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7618BCI based on Motor Imagery2009-08-27T18:07:01Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/05/2008<br />
<br />
End date: <br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:TizianoDalbis | Tiziano D'Albis]] (master student)<br />
* [[User:FabioBeltramini | Fabio Beltramini]] (bachelor student)<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Started studying material from course of Methodologies for Intelligent Systems<br />
** Started studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzed ErrP caused by classification results in a MI task<br />
** Deployed a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for use<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
* Week3:<br />
** Brand new interface for better stimulation of potentials developed<br />
** Major improvement in file management for Matlab offline analysis<br />
** Set up of acquisition software for the new interface<br />
** Development of scripts to approximatively determine the end of training phase in course<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7606BCI based on Motor Imagery2009-08-24T09:49:16Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/05/2008<br />
<br />
End date: <br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:TizianoDalbis | Tiziano D'Albis]] (master student)<br />
* [[User:FabioBeltramini | Fabio Beltramini]] (bachelor student)<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Started studying material from course of Methodologies for Intelligent Systems<br />
** Started studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzed ErrP caused by classification results in a MI task<br />
** Deployed a MI interface with visual synchronization signal<br />
* Week2:<br />
** Interface with discrete feedback presentation ready for use<br />
** Interface with early classification feedback ready for use<br />
** Started testing of newly created interfaces<br />
** Analizing P300+Errp code for merging<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7505BCI based on Motor Imagery2009-08-01T17:57:12Z<p>PaoloCalloni: /* Part 3: Project tracking */</p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/05/2008<br />
<br />
End date: <br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:TizianoDalbis | Tiziano D'Albis]] (master student)<br />
* [[User:FabioBeltramini | Fabio Beltramini]] (bachelor student)<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
Before 25/07/2009 some work as been done: <br />
* literature about motor imagery in BCI field has been read<br />
* initial sessions for signal analysis have been performed<br />
* a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
* some utilities to deal with large amount of data have been deployed<br />
* everything has been ported from Matlab to BCI2000<br />
* feedback sessions have proven the functionality of the previous work<br />
* a BCI speller has been tested with the output of the classifier<br />
<br />
After 25/07/2009:<br />
* Week1:<br />
** Started studying material from course of Methodologies for Intelligent Systems<br />
** Started studying of ERP for applications in motor imagery <br />
** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
** Analyzed ErrP caused by classification results in a MI task<br />
** Deployed a MI interface with visual synchronization signal<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=7504BCI based on Motor Imagery2009-08-01T17:56:09Z<p>PaoloCalloni: </p>
<hr />
<div>== '''Part 1: Project profile''' ==<br />
<br />
=== Project name ===<br />
<br />
BCI based on Motor Imagery<br />
<br />
=== Project short description ===<br />
<br />
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.<br />
<br />
=== Dates ===<br />
Start date: 01/05/2008<br />
<br />
End date: <br />
<br />
=== Website(s) ===<br />
<br />
http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery<br />
<br />
=== People involved ===<br />
<br />
<br />
===== Project head(s) =====<br />
<br />
* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)<br />
<br />
===== Other Politecnico di Milano people =====<br />
<br />
* [[User:RossellaBlatt | Rossella Blatt]] (phd student)<br />
<br />
===== Students currently working on the project =====<br />
<br />
* [[User:TizianoDalbis | Tiziano D'Albis]] (master student)<br />
* [[User:FabioBeltramini | Fabio Beltramini]] (bachelor student)<br />
* [[User:FabioZennaro | Fabio Massimo Zennaro]] (master student)<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.<br />
Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: Project description''' ==<br />
<br />
A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms. <br />
<br />
== '''Part 3: Project tracking''' ==<br />
<br />
* Before 25/07/2009 some work as been done: <br />
** literature about motor imagery in BCI field has been read<br />
** initial sessions for signal analysis have been performed<br />
** a feature extractor and a classifier have been trained to interpret brain's motor imagination<br />
** some utilities to deal with large amount of data have been deployed<br />
** everything has been ported from Matlab to BCI2000<br />
** feedback sessions have proven the functionality of the previous work<br />
** a BCI speller has been tested with the output of the classifier<br />
* After 25/07/2009:<br />
** Week1:<br />
*** Started studying material from course of Methodologies for Intelligent Systems<br />
*** Started studying of ERP for applications in motor imagery <br />
*** Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.<br />
*** Analyzed ErrP caused by classification results in a MI task<br />
*** Deployed a MI interface with visual synchronization signal<br />
<br />
== '''Part 4: References''' ==<br />
<br />
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.<br />
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791<br />
* EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.<br />
<br />
<br />
== Links ==<br />
<br />
* [[Brain-Computer Interface]] page on this Wiki<br />
* [[Electroencephalographs]]<br />
* [[How to mount electrodes]]<br />
* [[How to setup BCI software]]</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=File:Paolocalloni.jpg&diff=7503File:Paolocalloni.jpg2009-08-01T17:29:39Z<p>PaoloCalloni: uploaded a new version of "Image:Paolocalloni.jpg"</p>
<hr />
<div></div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=File:Paolocalloni.jpg&diff=7502File:Paolocalloni.jpg2009-08-01T17:28:07Z<p>PaoloCalloni: uploaded a new version of "Image:Paolocalloni.jpg"</p>
<hr />
<div></div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=User:PaoloCalloni&diff=5440User:PaoloCalloni2009-03-09T09:24:09Z<p>PaoloCalloni: New page: {{SMWUser |firstname=Paolo |lastname=Calloni |email=paolo(dot)calloni(at)gmail(dot)com |advisor=RossellaBlatt |projectpage=BCI based on Motor Imagery |photo=paolocalloni.jpg}} I am a Mast...</p>
<hr />
<div>{{SMWUser<br />
|firstname=Paolo<br />
|lastname=Calloni<br />
|email=paolo(dot)calloni(at)gmail(dot)com<br />
|advisor=RossellaBlatt<br />
|projectpage=BCI based on Motor Imagery<br />
|photo=paolocalloni.jpg}}<br />
<br />
I am a Master Student in Computer Engineering at Politecnico di Milano. <br/><br />
I'm currently working on my master thesis in the field of Brain Computer Interfaces based on Motor imagery with [[User:RossellaBlatt | Rossella Blatt]] and [[User:MatteoMatteucci | Matteo Matteucci]].<br/></div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=File:Paolocalloni.jpg&diff=5439File:Paolocalloni.jpg2009-03-09T09:22:24Z<p>PaoloCalloni: uploaded a new version of "Image:Paolocalloni.jpg"</p>
<hr />
<div></div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=File:Paolocalloni.jpg&diff=5438File:Paolocalloni.jpg2009-03-09T09:20:16Z<p>PaoloCalloni: </p>
<hr />
<div></div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Automated_extraction_of_laser_streaks_and_range_profiles&diff=3966Automated extraction of laser streaks and range profiles2008-09-17T12:35:49Z<p>PaoloCalloni: /* Dates */</p>
<hr />
<div>== '''Part 1: project profile''' ==<br />
<br />
=== Project name ===<br />
''Automated extraction of laser streaks and range profiles''<br />
<br />
=== Project short description ===<br />
''The aim of this project is to develop an algorithm to extract laser traces produced on various surfaces by a laser scanner from images taken by a camera without IR filter''<br />
<br />
=== Dates ===<br />
Start date: 2008/04/10<br />
<br />
End date: 2008/06/25<br />
<br />
=== People involved ===<br />
==== Project head(s) ====<br />
<br />
V. Caglioti - Vincenzo (dot) Caglioti (at) polimi (dot) it<br />
<br />
==== Other Politecnico di Milano people ====<br />
D. Migliore - migliore (at) elet (dot) polimi (dot) it<br />
<br />
A. Giusti - Alessandro (dot) giusti (at) polimi (dot) it<br />
<br />
==== Students ====<br />
Paolo Calloni - paolo (dot) calloni (at) mail (dot) polimi (dot) it<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab/Lambrate. It will include significant amounts of mechanical work as well as of electrical and electronic activity. Potentially risky activities are the following:<br />
* Use of Lasers. Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: project description''' ==<br />
<br />
=== Tasks ===<br />
* Shoot two images with the camera and laser scanner fixed, one with the scanner on and one with the scanner off. Experiment different lighting conditions and camera settings.<br />
* Try subtracting the two images; the trace should be barely visible.<br />
* Equalize and smooth the resulting image<br />
* Return the trace as a set of rectilinear and curvilinear segments.<br />
<br />
=== Instrumentation in use (returned 07/08) ===<br />
* Hokuyo Laser scanner URG-04LX<br />
* Unibrain digital camera + standard 42.5° lens</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Automated_extraction_of_laser_streaks_and_range_profiles&diff=3965Automated extraction of laser streaks and range profiles2008-09-17T12:35:19Z<p>PaoloCalloni: /* Instrumentation in use (to be returned) */</p>
<hr />
<div>== '''Part 1: project profile''' ==<br />
<br />
=== Project name ===<br />
''Automated extraction of laser streaks and range profiles''<br />
<br />
=== Project short description ===<br />
''The aim of this project is to develop an algorithm to extract laser traces produced on various surfaces by a laser scanner from images taken by a camera without IR filter''<br />
<br />
=== Dates ===<br />
Start date: 2008/04/10<br />
<br />
End date: 2008/??/??<br />
<br />
=== People involved ===<br />
==== Project head(s) ====<br />
<br />
V. Caglioti - Vincenzo (dot) Caglioti (at) polimi (dot) it<br />
<br />
==== Other Politecnico di Milano people ====<br />
D. Migliore - migliore (at) elet (dot) polimi (dot) it<br />
<br />
A. Giusti - Alessandro (dot) giusti (at) polimi (dot) it<br />
<br />
==== Students ====<br />
Paolo Calloni - paolo (dot) calloni (at) mail (dot) polimi (dot) it<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab/Lambrate. It will include significant amounts of mechanical work as well as of electrical and electronic activity. Potentially risky activities are the following:<br />
* Use of Lasers. Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: project description''' ==<br />
<br />
=== Tasks ===<br />
* Shoot two images with the camera and laser scanner fixed, one with the scanner on and one with the scanner off. Experiment different lighting conditions and camera settings.<br />
* Try subtracting the two images; the trace should be barely visible.<br />
* Equalize and smooth the resulting image<br />
* Return the trace as a set of rectilinear and curvilinear segments.<br />
<br />
=== Instrumentation in use (returned 07/08) ===<br />
* Hokuyo Laser scanner URG-04LX<br />
* Unibrain digital camera + standard 42.5° lens</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Automated_extraction_of_laser_streaks_and_range_profiles&diff=2311Automated extraction of laser streaks and range profiles2008-04-16T19:54:44Z<p>PaoloCalloni: </p>
<hr />
<div>== '''Part 1: project profile''' ==<br />
<br />
=== Project name ===<br />
''Automated extraction of laser streaks and range profiles''<br />
<br />
=== Project short description ===<br />
''The aim of this project is to develop an algorithm to extract laser traces produced on various surfaces by a laser scanner from images taken by a camera without IR filter''<br />
<br />
=== Dates ===<br />
Start date: 2008/04/10<br />
<br />
End date: 2008/??/??<br />
<br />
=== People involved ===<br />
==== Project head(s) ====<br />
<br />
V. Caglioti - Vincenzo (dot) Caglioti (at) polimi (dot) it<br />
<br />
==== Other Politecnico di Milano people ====<br />
D. Migliore - migliore (at) elet (dot) polimi (dot) it<br />
<br />
A. Giusti - Alessandro (dot) giusti (at) polimi (dot) it<br />
<br />
==== Students ====<br />
Paolo Calloni - paolo (dot) calloni (at) mail (dot) polimi (dot) it<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab/Lambrate. It will include significant amounts of mechanical work as well as of electrical and electronic activity. Potentially risky activities are the following:<br />
* Use of Lasers. Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: project description''' ==<br />
<br />
=== Tasks ===<br />
* Shoot two images with the camera and laser scanner fixed, one with the scanner on and one with the scanner off. Experiment different lighting conditions and camera settings.<br />
* Try subtracting the two images; the trace should be barely visible.<br />
* Equalize and smooth the resulting image<br />
* Return the trace as a set of rectilinear and curvilinear segments.<br />
<br />
=== Instrumentation in use (to be returned) ===<br />
* Hokuyo Laser scanner URG-04LX<br />
* Unibrain digital camera + standard 42.5° lens</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Automated_extraction_of_laser_streaks_and_range_profiles&diff=1942Automated extraction of laser streaks and range profiles2008-03-31T07:49:00Z<p>PaoloCalloni: </p>
<hr />
<div>== '''Part 1: project profile''' ==<br />
<br />
=== Project name ===<br />
''Automated extraction of laser streaks and range profiles''<br />
<br />
=== Project short description ===<br />
''The aim of this project is to develop an algorithm to extract laser traces produced on various surfaces by a laser scanner from images taken by a camera without IR filter''<br />
<br />
=== Dates ===<br />
Start date: 2008/04/??<br />
<br />
End date: 2008/??/??<br />
<br />
=== People involved ===<br />
==== Project head(s) ====<br />
<br />
V. Caglioti - Vincenzo (dot) Caglioti (at) polimi (dot) it<br />
<br />
==== Other Politecnico di Milano people ====<br />
D. Migliore - migliore (at) elet (dot) polimi (dot) it<br />
<br />
A. Giusti - Alessandro (dot) giusti (at) polimi (dot) it<br />
<br />
==== Students ====<br />
Paolo Calloni - paolo (dot) calloni (at) mail (dot) polimi (dot) it<br />
<br />
=== Laboratory work and risk analysis ===<br />
<br />
Laboratory work for this project will be mainly performed at AIRLab/Lambrate. It will include significant amounts of mechanical work as well as of electrical and electronic activity. Potentially risky activities are the following:<br />
* Use of Lasers. Standard safety measures described in [http://airlab.elet.polimi.it/index.php/airlab/content/download/461/4110/file/documento_valutazione_rischi_AIRLab.pdf Safety norms] will be followed.<br />
<br />
== '''Part 2: project description''' ==<br />
<br />
=== Tasks ===<br />
* Shoot two images with the camera and laser scanner fixed, one with the scanner on and one with the scanner off. Experiment different lighting conditions and camera settings.<br />
* Try subtracting the two images; the trace should be barely visible.<br />
* Equalize and smooth the resulting image<br />
* Return the trace as a set of rectilinear and curvilinear segments.</div>PaoloCallonihttps://airwiki.elet.polimi.it/index.php?title=Projects&diff=1941Projects2008-03-31T07:48:05Z<p>PaoloCalloni: </p>
<hr />
<div>This page is a repository of links to the pages describing the '''projects''' we at the AIRLab are working on, or have been in the past.<br />
<br />
== Ongoing projects ==<br />
''by research area (areas are defined in the [[Main Page]]); for each project a name and a link to its AIRWiki page is given''<br />
<br />
==== [[Agents, Multiagent Systems, Agencies]] ====<br />
----<br />
<br />
Multiagent cooperating system [[Multiagent cooperation]]<br />
<br />
==== [[BioSignal Analysis]] ====<br />
----<br />
====== [[Affective Computing]] ======<br />
<br />
* [[Driving companions]]<br />
<br />
* [[Affective Robotic Rehabilitation]]<br />
<br />
====== [[Brain Computer Interface]] ======<br />
<br />
* [[Command wheelchair using BCI2000]]<br />
<br />
====== [[Automatic Detection Of Sleep Stages]] ======<br />
<br />
* [[Sleep Staging with HMM]]<br />
<br />
==== [[Computer Vision and Image Analysis]] ====<br />
----<br />
<br />
* [[Automated extraction of laser streaks and range profiles]]<br />
<br />
* [[Data collection for mutual calibration|Data collection for laser-rangefinder and camera calibration]]<br />
<br />
* [[Particle filter for object tracking]]<br />
<br />
==== [[E-Science]] ====<br />
----<br />
==== [[Machine Learning]] ====<br />
----<br />
==== [[Ontologies and Semantic Web]] ====<br />
----<br />
* [[JOFS|JOFS, Java Owl File Storage]]<br />
* [[FolksOnt|FolksOnt]]<br />
<br />
==== [[Philosophy of Artificial Intelligence]] ====<br />
----<br />
==== [[Robotics]] ====<br />
----<br />
* [[LURCH - The autonomous wheelchair]]<br />
<br />
* [[Rawseeds|RAWSEEDS]]<br />
<br />
* [[Balancing robots: Tilty, TiltOne]]<br />
<br />
==== [[Soft Computing]] ====<br />
----<br />
<br />
== Finished projects ==<br />
''by research area (areas are defined in the [[Main Page]]; for each project a name and a link to its AIRWiki page is given''<br />
<br />
==== [[Agents, Multiagent Systems, Agencies]] ====<br />
<br />
==== [[BioSignal Analysis]] ====<br />
<br />
==== [[Computer Vision and Image Analysis]] ====<br />
<br />
==== [[E-Science]] ====<br />
<br />
==== [[Machine Learning]] ====<br />
<br />
==== [[Ontologies and Semantic Web]] ====<br />
<br />
* [http://smw.elet.polimi.it Swiki] (external link)<br />
* [[Design and development of a semantic wiki engine]]<br />
* [[Sandbox ontologies for semantic wikis]]<br />
* [[Tagonto]]<br />
* [[SpeakinAbout|Speakin'About - a tool for semantic annotation of text documents]]<br />
* [[Tag based filesystems]]<br />
* [[Semantic wiki extensions with context ontologies]]<br />
* [[BinaryTags|Binary tag system for the creation of relations in a semantic wiki]]<br />
<br />
==== [[Philosophy of Artificial Intelligence]] ====<br />
<br />
==== [[Robotics]] ====<br />
<br />
==== [[Soft Computing]] ====<br />
<br />
<br><br />
<br />
== Note for students ==<br />
<br />
If you are a student and there isn't a '''page describing your project''', this is because YOU have the task of creating it and populating it with (meaningful) content. If you are a student and there IS a page describing your project, you have the task to complete that page with (useful and comprehensive) information about you and your own contribution to the project. <br />
<br />
Be aware that you can work within the AIRLab's structures (see [[The Labs]]) ''only after the page of your project has been set up AND completed with all the above information''. Be also aware that the quality of your work (or lack of it) on the AIRWiki will be evaluated by the Teachers and will influence your grades.<br />
<br />
Instructions to add a new project are available at [[#HOWTO add a new project to the AIRWiki]].<br />
<br />
== HOWTO add a new project to the AIRWiki ==<br />
'''NOTE: if you have difficulties with the English language, it is MUCH better to ask for help while reading the following instructions than to proceed anyway and do some ugly mistake with the work of other people.''' (Of course, if you are not able to read this NOTE we are doomed.)<br />
<br />
Contributing to a wiki is easy, and leaves you with a deep sense of satisfaction: by contributing, you are documenting your work in a durable form ''and'' helping all the other users of the wiki as well.<br />
<br />
Of course, to contribute you need an '''AIRWiki account''' (the wiki is open only to teachers and students working at the AIRLab): the instructions to get one are on the [[Main Page]]. By the way, a well thought out help for MediaWiki (the software that AIRWiki, or for that matter also Wikipedia and many other websites, is built upon) is available [http://www.mediawiki.org/wiki/Help:Contents here]. So even if this is your first experience with a wiki, you should not have any problems.<br />
Please note that all the content you insert into the AIRWiki must be written ''in English''.<br />
<br />
Here is a complete description of the procedure to create a new page associated to your project: follow its steps carefully, because other contributors do not appreciate when their content is damaged or destroyed by careless people. Be ''very'' careful with that 'Save page' button...<br />
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'''Preparing the new project page'''<br />
# open a text editor (e.g. Notepad);<br />
# create a new, empty text file: let's call it ''YourPage.txt'';<br />
# open the [[Project page template]] AIRWiki page with a browser (e.g. Firefox): the internet address is http://airwiki.elet.polimi.it/mediawiki/index.php/Project_page_template;<br />
# click the ''edit'' tab on the top of the [[Project page template]] to expose the wiki source text;<br />
# copy all the content of the source text window into YourPage.txt, e.g. by using Ctrl+C, Ctrl+V. '''Make sure not to cut or alter the source text of the [[Project page template]]! Do NOT click on the 'Save page' button!'''<br />
# close your browser;<br />
# modify the YourPage.txt file by substituting all the example text with information about your project. It is likely that, for the time being, Part 2 of the page (project description) will be empty: after all you just started working on the project, isn't it?<br />
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'''Adding the new project to the [[Projects]] page of the AIRWiki'''<br />
# with a browser, open the Projects page: the internet address is http://airwiki.elet.polimi.it/mediawiki/index.php/Projects;<br />
# go to the 'Ongoing projects' section and find the subsection having the name of the research area of your project: e.g. "E-Science" (if you have doubts, ask the teachers);<br />
#choose a name of your liking for the new AIRWiki page dedicated to your project: use a word or a short phrase with only the first letter in capitals (of course you have to choose a name that is coherent with the objectives of the project);<br />
# click the ''edit'' link on the right of the subsection to expose the wiki source text;<br />
# add a new text line with the name of your project and a link to its wiki page: the latter is simply the page name you chose surrounded by double square parentheses (this will create the page when you will click the "Save page" button - DON'T click it now);<br />
# use a blank line to separate the new line from pre-existing text; <br />
#'''be extremely careful not to alter pre-existing text: if you think you could have done that, press the 'back' button of your browser now to exit from the editing page without saving''', then repeat the editing steps;<br />
# click the "Show preview" button at the bottom of the page, and look carefully at the whole subsection (not only to the part you added): if it doesn't seem to be ''perfectly right'', press the 'back' button of your browser to exit from the editing page without saving, then repeat the editing;<br />
# when you are certain that all is ok, click the "Save page" button at the bottom of the page.<br />
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If you didn't screw up, you should be now (proudly) looking at the description of your project, perfectly set among the others.<br />
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Now, clicking on the link to your project's page in the [[Projects]] page of the AIRWiki, you will see one of these:<br />
* the 'edit' tab of an empty page: good, you chose a good name for your page. Proceed to fill the page as described below.<br />
* a non-blank page: argh, you chose an already-used name for your page. Re-edit the Projects page and modify the link (i.e. the name between double square parentheses) you put in it, changing the name of your new page (i.e. again, the name between double square parentheses). As before, click the "Save page" button '''only if and when you are certain that all is ok in the whole page'''.<br />
Go on with this checking and modifying until you find a (sensible!) name that no other project has yet used.<br />
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'''Filling the new page'''<br />
# If you are looking at the 'edit' tab of your project's new page, simply open file "YourPage.txt" (you know, the one containing the content for your page, which you prepared before) and copy its entire contents into the page. If not, first use a browser to open your page (the internet address is http://airwiki.elet.polimi.it/mediawiki/index.php/NameOfYourPage) and click on the 'edit' tab on the top of the page.<br />
# click the "Show preview" button at the bottom of the page, and look carefully at the result: if it doesn't seem to be right, press the 'back' button of your browser now to exit from the editing page without saving, then repeat the page editing by clicking on the 'edit' tab on the top of the page;<br />
# when you are certain that all is ok, click the "Save page" button at the bottom of the page. <br />
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You should now be able to (veeery proudly) see your project's page in its full glory. If you aren't satisfied with the result, just go back to page editing by clicking on the 'edit' tab on the top of the page.<br />
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As your work on your project will go on, don't forget to keep your project's page up-to-date by editing it every time you have new material. Remember that for your teachers it will be the main source of information about how your project is going, so they will look at it often and with attention :-)</div>PaoloCalloni