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| − | == '''Part 1: Project profile''' == | + | {{Project |
| | + | |title=BCI based on Motor Imagery |
| | + | |short_descr=This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp. |
| | + | |coordinator=MatteoMatteucci |
| | + | |tutor=MatteoMatteucci;RossellaBlatt;BernardoDalSeno |
| | + | |students=FabioZennaro |
| | + | |resarea=BioSignal Analysis |
| | + | |status=Closed |
| | + | }} |
| | + | == '''Website(s)''' == |
| | | | |
| − | === Project name === | + | [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] |
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| − | BCI based on Motor Imagery | + | [http://airwiki.elet.polimi.it/mediawiki/index.php/Brain-Computer_Interface BCI Projects on AirWiki] |
| | | | |
| − | === Project short description === | + | == '''Project description''' == |
| | | | |
| − | This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.
| + | 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. |
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| − | === Dates ===
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| − | Start date: 01/05/2008
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| − | End date:
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| − | === Website(s) === | + | == '''References''' == |
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| − | http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery
| + | * 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. |
| | + | * Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791 |
| | + | * 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. |
| | | | |
| − | === People involved === | + | == '''Links''' == |
| − | ''put here the links to the AIRWiki pages associated to all the people working on the project'' | + | |
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| − | * [[User:RossellaBlatt | Rossella Blatt]] (phd student) | + | * [[Electroencephalographs]] |
| − | * [[User:TizianoDalbis | Tiziano D'Albis]] (master student) | + | * [[How to mount electrodes]] |
| − | * [[User:FabioBeltramini | Fabio Beltramini]] (bachelor student) | + | * [[How to setup BCI software]] |
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| − | ===== Project head(s) ===== | + | == '''External Links''' == |
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| − | * [[User:MatteoMatteucci | Matteo Matteucci]] (professor) | + | * [http://www.bci2000.org/BCI2000/Home.html BCI2000 Website] |
| − | | + | * [http://www.bci2000.org/wiki/index.php/Main_Page BCI2000 Wiki] |
| − | ===== Other Politecnico di Milano people =====
| + | |
| − | | + | |
| − | * [[User:RossellaBlatt | Rossella Blatt]] (phd student)
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| − | | + | |
| − | ===== Students currently working on the project =====
| + | |
| − | | + | |
| − | * [[User:TizianoDalbis | Tiziano D'Albis]] (master student)
| + | |
| − | * [[User:FabioBeltramini | Fabio Beltramini]] (bachelor student)
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| − | | + | |
| − | === Laboratory work and risk analysis ===
| + | |
| − | | + | |
| − | 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.
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| − | 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.
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| − | | + | |
| − | == '''Part 2: Project description''' ==
| + | |
| − | | + | |
| − | 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, controllinh his/her mu or beta rhythms.
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| − | | + | |
| − | === How to set up BCI instrumentation ===
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| − | | + | |
| − | You’ll nedd:
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| − | | + | |
| − | * Electroencephalograph (with PCMCIA connector)
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| − | * Laptop (ensures that the AC adapter is NOT connected, for safety reasons)
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| − | * Electrodes or an Electrodes Cap (designed for the 10-20 system)
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| − | * Conductive gel and/or abrasive electrode-gel
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| − | * Tape measure to put in the correct position the electrodes
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| − | * Adhesive tape (if using single electrodes to fix them)
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| − | * Syringe (if using electrodes cap, to insert the conductive gel into the electrodes hole)
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| − | * Fototransistor (if necessary)
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| − | | + | |
| − | Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Mobile phones can be another source of noise: it is better to switch them off before while performing the acquisition.
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| − | In order to acquire the brain signal, it is possible to use an electrodes cap, or a set of separated single electrodes.
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| − | | + | |
| − | ==== Electrodes Cap ====
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| − | | + | |
| − | Choose the suitable size for the patient, and then put the cap on his head in the correct position:
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| − | | + | |
| − | * measure his head size (from the nasion NS to the inion) this is the 100% of the measure
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| − | * now you can put the CZ electrode at 50% of the entire measure, and check the positions of the other electrodes (i.e. FP1 and FP2 should be at 10% of the measure from nasion) .
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| − | * fill in the electrodes with conductive gel (use the syringe), take care that hair don’t disturb the skin-electrode contact.
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| − | * remember to fill with condutictive gel also to electrodes cabled in red (NE=Neutral Electrode) and green (ISOGND=Isolated Ground) that are used respectively as reference (RF) and ground values in the measurements.
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| − | * connect the cable to the Electroencephalograph.
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| − | | + | |
| − | ==== Separeted electrodes ====
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| − | | + | |
| − | If you haven’t got an electrodes cap you can position them one by one following this figure, with the relatives measures of each electrode:
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| − | | + | |
| − | [[Image:10-20System.jpg]]
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| − | | + | |
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| − | Connect the reference electrode to the forehead and the GND electrode to the right earlobe.
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| − | Before connecting one electrode, put a little bit of abrasive gel on a cotton fioc and scratch the portion of patient’s skin where you’ll put the electrode.
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| − | Fill in the electrode with a little bit of conductive gel, and put it on the skin, in the correct position.
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| − | Fix the cable with adhesive tape, and connect it to the Electroencephalograph.
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| − | | + | |
| − | ==== EOG ====
| + | |
| − | | + | |
| − | If you want to eliminate EOG artefacts from your data acquisitions, you would record EOG signal putting 2 more electrodes, near the patient eye.
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| − | One electrode just over the eyebrow and one under the eye (REF) , like in the figure:
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| − | | + | |
| − | FIGURA elettrodi occhi
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| − | | + | |
| − | Connect the EOG cables to the A-Channel of the Electroencephalograph.
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| − | | + | |
| − | ==== Impedance check ====
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| − | | + | |
| − | Now you can start Galileo Software and check if the correct values of impedance are measured.
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| − | Click on the button “OHMMETER” in the upper toolbar, and the system will display the calculated value for each electrode, both in “graphical” form and in “numerical” one.
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| − | Values under 10 KOhm are acceptable, values under 5 KOhm are very good.
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| − | If your values aren’t under 10KOhm try to decrease them, filling the electrodes with more conductive gel until you reach acceptable values.
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| − | | + | |
| − | === References ===
| + | |
| − | * 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.
| + | |
| − | * Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791
| + | |
| − | * 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.
| + | |
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.
