https://airwiki.elet.polimi.it/api.php?action=feedcontributions&feedformat=atom&user=FabioBeltramini 2026-04-15T09:16:57Z User contributions MediaWiki 1.25.6 https://airwiki.elet.polimi.it/index.php?title=Camcorder&diff=5172 2009-02-12T10:46:51Z

<p>FabioBeltramini: /* Where is the camera now */</p> <hr /> <div>In the AIRLab you can find also a ''memory camcorder'', i.e. a consumer camera system that records (lossy) compressed digital video onto standard SD flash memory cards. It's a [http://www.samsung.com/uk/support/download/supportDown.do?group=homeentertainment&amp;type=camcorder&amp;subtype=flashcamcorder&amp;model_nm=VP-MX10&amp;prd_ia_cd=03110800&amp;disp_nm=VP-MX10&amp;mType=&amp;dType=D&amp;vType=R Samsung - VP-MX10H] and can be used to record on video demos, lessons or talks. It is fitted with an 8GB SD card, i.e. the biggest it can be used with.<br /> <br /> Main features:<br /> * maximum resolution 720x576 pixel (progressive scan), the same as DVD<br /> * MPEG4 encoding (Mplayer for Linux, for example, plays it back perfectly)<br /> * 2.7&quot; 16:9 LCD Display (but recorded image format is 4:3)<br /> * 34x Optical zoom (1200x with digital zoom)<br /> * image stabilizer (use it if you go over, say, 4x of zoom factor)<br /> * USB connection to PC (it's seen as a mass storage device)<br /> * maximum usable SD card capacity 8GB<br /> * 220' of video on an 8GB card in best-quality mode<br /> * 120' (claimed) battery duration (with fully charged battery, during recording and if you don't play much with the zoom)<br /> * unreliable battery charge indicator :-(<br /> * battery discharges quickly even when the device is off, so be sure to recharge it before use<br /> <br /> ===Where is the camera now===<br /> The camera is in AI&amp;R Lab, in Lambrate from February 12th.<br /> <br /> == Booking ==<br /> <br /> If you want to use the camera, please book in advance by adding an entry to the table.<br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || [[User:DonaldDuck]]<br /> |- <br /> | Friday 15 April || 9:30-13:00 || [[User:MickeyMouse]]<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> <br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person<br /> |-<br /> |10 Feb || 9:30-15:00 || [[User:BernardoDalSeno]]<br /> |-<br /> |}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Lung_Cancer_Detection_by_an_Electronic_Nose&diff=5153 2009-02-10T16:18:54Z

<p>FabioBeltramini: /* External personnel: */</p> <hr /> <div>== '''Part 1: project profile''' ==<br /> <br /> === Project name ===<br /> <br /> Lung Cancer Detection by an Electronic Nose<br /> <br /> === Project description ===<br /> <br /> The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification process based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. In our work, we used an array composed of six Metal Oxide Semiconductor (MOS) sensors.<br /> In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method. <br /> <br /> During a first pilot study of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the<br /> olfactory signal associated to exhalations of subjects. Results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from<br /> different types of lung cancer (primary and not) at different stages.<br /> In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ at best, and to reduce the dimensionality<br /> of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric<br /> Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based<br /> on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers<br /> and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation. <br /> <br /> The achieved satisfactory results pushed us to begin a new study, in roder to confirm the obtained promising results and to evaluate the ripetibility of our results. We analyzed 104 breath samples of 52 subjects, 22 healthy subjects and 30 subjects with primary lung cancer at different stages. The acquisition has been done inviting subjects to breath into a nalophan bag, later input into the electronic nose. In order to find the best statistical model able to discriminate between the two classes ‘healthy’ and ‘lung cancer’ subjects, and to reduce the dimensionality of the problem, we implemented a genetic algorithm (GA) that found the best combination of feature selection, feature projection and classifier. In particular, according to the feature selection issue, we considered methods based on exponential, sequential and randomized algorithms. Principal Component Analysis (PCA), Fisher’s Linear Discriminant Analysis (LDA) and Non Parametric Linear Discriminant Analysis (NPLDA) have been considered to project features into a lower dimensional space. Classification has been performed implementing several supervised pattern classification algorithms, based on different k-nearest neighbors (k-NN) approaches (classic, modified and fuzzy k-NN), on linear and quadratic discriminant classifiers and on a feed-forward artificial neural network (ANN). The best solution provided from the genetic algorithm, has been the projection of the found subset of features into a single component using the Fisher’s Linear Discriminant Analysis (LDA) and a classification based on the k-Nearest Neighbours (k-NN) method. Performing a Student’s t-test between all pair of considered models, no significative differences emerged, suggesting that all computational intelligence methods that we have applied provided satisfying results. The observed results, all validated using cross-validation, have been very satisfactory achieving an average accuracy of 96.2%, an average sensitivity of 93.3% and an average specificity of 100%, as well as very small confidence intervals. These results confirmed a previous pilot study where we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5% (on 58 control subjects and 43 lung cancer subjects). We also investigated the possibility of performing early diagnosis, building a model able to predict a sample belonging to a subject with primary lung cancer at stage I, compared to healthy subjects. Also in this analysis results have been excellent, achieving an average accuracy of 92.85%, an average sensitivity of 75.5% and an average specificity of 97.72%. <br /> <br /> The research demonstrate that an instrument as the electronic nose, combined with the appropriate artificial intelligence techniques, is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. Moreover, the instrument is completely non invasive. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.<br /> <br /> === Dates ===<br /> Start date: 2007/01/01<br /> <br /> End date: --<br /> <br /> === Website(s) ===<br /> <br /> At the moment no website avaible<br /> <br /> === People involved ===<br /> <br /> ===== Project head(s) =====<br /> <br /> A. Bonarini - [[User:AndreaBonarini]]<br /> <br /> M. Matteucci - [[User:MatteoMatteucci]]<br /> <br /> ===== PhD Students =====<br /> <br /> R. Blatt - [[User:RossellaBlatt]]<br /> <br /> ===== Students currently working on the project =====<br /> <br /> Claudio Trameri - [[User:ClaudioTrameri]]<br /> <br /> Mauro Verdirosa - [[User:MauroVerdirosa]]<br /> <br /> ===== Students who worked on the project in the past =====<br /> <br /> ===== External personnel: =====<br /> <br /> Dott. Ugo Pastorino (Istituto dei Tumori - Milano)<br /> <br /> Dott. Elisa Calabrò (Istituto dei Tumori - Milano)<br /> <br /> === Laboratory work and risk analysis ===<br /> <br /> Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done. <br /> For this kind of work, there are not potential risks.<br /> <br /> == '''Part 2: project description''' ==<br /> <br /> === State of the art ===<br /> <br /> === Preliminary and sketches ===<br /> <br /> === Design notes and guidelines ===<br /> <br /> === Link to project documents and files ===<br /> <br /> Results obtained from this work have been presented at different conferences:<br /> <br /> * '''Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece''' <br /> :The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th. <br /> :[[Image:PAIS.pdf|Paper-PAIS2008]] <br /> <br /> * '''International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA'''<br /> :'''Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: [[Image:IJCNNfinal.pdf|Paper-IJCNN2007]] <br /> <br /> :Short presentation of the ''Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors'' paper: [[Image:LungCancerIdentificationIJCNN2007.pdf|Presentation-IJCNN2007]]<br /> <br /> * '''International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy'''<br /> <br /> : '''Fuzzy k-NN Lung Cancer Identification by an Electronic Nose''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.<br /> <br /> === Description and results of experiments ===<br /> <br /> === Photos and videos ===<br /> <br /> === Link to source code of the software written for the project ===<br /> <br /> === Description and results of experiments ===<br /> <br /> === Useful internet links ===</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Lung_Cancer_Detection_by_an_Electronic_Nose&diff=5152 2009-02-10T16:18:26Z

<p>FabioBeltramini: /* Other Politecnico di Milano people */</p> <hr /> <div>== '''Part 1: project profile''' ==<br /> <br /> === Project name ===<br /> <br /> Lung Cancer Detection by an Electronic Nose<br /> <br /> === Project description ===<br /> <br /> The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification process based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. In our work, we used an array composed of six Metal Oxide Semiconductor (MOS) sensors.<br /> In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method. <br /> <br /> During a first pilot study of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the<br /> olfactory signal associated to exhalations of subjects. Results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from<br /> different types of lung cancer (primary and not) at different stages.<br /> In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ at best, and to reduce the dimensionality<br /> of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric<br /> Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based<br /> on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers<br /> and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation. <br /> <br /> The achieved satisfactory results pushed us to begin a new study, in roder to confirm the obtained promising results and to evaluate the ripetibility of our results. We analyzed 104 breath samples of 52 subjects, 22 healthy subjects and 30 subjects with primary lung cancer at different stages. The acquisition has been done inviting subjects to breath into a nalophan bag, later input into the electronic nose. In order to find the best statistical model able to discriminate between the two classes ‘healthy’ and ‘lung cancer’ subjects, and to reduce the dimensionality of the problem, we implemented a genetic algorithm (GA) that found the best combination of feature selection, feature projection and classifier. In particular, according to the feature selection issue, we considered methods based on exponential, sequential and randomized algorithms. Principal Component Analysis (PCA), Fisher’s Linear Discriminant Analysis (LDA) and Non Parametric Linear Discriminant Analysis (NPLDA) have been considered to project features into a lower dimensional space. Classification has been performed implementing several supervised pattern classification algorithms, based on different k-nearest neighbors (k-NN) approaches (classic, modified and fuzzy k-NN), on linear and quadratic discriminant classifiers and on a feed-forward artificial neural network (ANN). The best solution provided from the genetic algorithm, has been the projection of the found subset of features into a single component using the Fisher’s Linear Discriminant Analysis (LDA) and a classification based on the k-Nearest Neighbours (k-NN) method. Performing a Student’s t-test between all pair of considered models, no significative differences emerged, suggesting that all computational intelligence methods that we have applied provided satisfying results. The observed results, all validated using cross-validation, have been very satisfactory achieving an average accuracy of 96.2%, an average sensitivity of 93.3% and an average specificity of 100%, as well as very small confidence intervals. These results confirmed a previous pilot study where we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5% (on 58 control subjects and 43 lung cancer subjects). We also investigated the possibility of performing early diagnosis, building a model able to predict a sample belonging to a subject with primary lung cancer at stage I, compared to healthy subjects. Also in this analysis results have been excellent, achieving an average accuracy of 92.85%, an average sensitivity of 75.5% and an average specificity of 97.72%. <br /> <br /> The research demonstrate that an instrument as the electronic nose, combined with the appropriate artificial intelligence techniques, is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. Moreover, the instrument is completely non invasive. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.<br /> <br /> === Dates ===<br /> Start date: 2007/01/01<br /> <br /> End date: --<br /> <br /> === Website(s) ===<br /> <br /> At the moment no website avaible<br /> <br /> === People involved ===<br /> <br /> ===== Project head(s) =====<br /> <br /> A. Bonarini - [[User:AndreaBonarini]]<br /> <br /> M. Matteucci - [[User:MatteoMatteucci]]<br /> <br /> ===== PhD Students =====<br /> <br /> R. Blatt - [[User:RossellaBlatt]]<br /> <br /> ===== Students currently working on the project =====<br /> <br /> Claudio Trameri - [[User:ClaudioTrameri]]<br /> <br /> Mauro Verdirosa - [[User:MauroVerdirosa]]<br /> <br /> ===== Students who worked on the project in the past =====<br /> <br /> ===== External personnel: =====<br /> <br /> Dott. Ugo Pastorino (Istituto dei Tumori - Milano)<br /> <br /> Dott. Elisa Calabrò (Istituto dei Tumori - Milano)<br /> <br /> Dott. Matteo Della Torre (SACMI - Imola)<br /> <br /> === Laboratory work and risk analysis ===<br /> <br /> Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done. <br /> For this kind of work, there are not potential risks.<br /> <br /> == '''Part 2: project description''' ==<br /> <br /> === State of the art ===<br /> <br /> === Preliminary and sketches ===<br /> <br /> === Design notes and guidelines ===<br /> <br /> === Link to project documents and files ===<br /> <br /> Results obtained from this work have been presented at different conferences:<br /> <br /> * '''Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece''' <br /> :The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th. <br /> :[[Image:PAIS.pdf|Paper-PAIS2008]] <br /> <br /> * '''International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA'''<br /> :'''Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: [[Image:IJCNNfinal.pdf|Paper-IJCNN2007]] <br /> <br /> :Short presentation of the ''Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors'' paper: [[Image:LungCancerIdentificationIJCNN2007.pdf|Presentation-IJCNN2007]]<br /> <br /> * '''International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy'''<br /> <br /> : '''Fuzzy k-NN Lung Cancer Identification by an Electronic Nose''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.<br /> <br /> === Description and results of experiments ===<br /> <br /> === Photos and videos ===<br /> <br /> === Link to source code of the software written for the project ===<br /> <br /> === Description and results of experiments ===<br /> <br /> === Useful internet links ===</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Lung_Cancer_Detection_by_an_Electronic_Nose&diff=5151 2009-02-10T16:17:34Z

<p>FabioBeltramini: /* Project short description */</p> <hr /> <div>== '''Part 1: project profile''' ==<br /> <br /> === Project name ===<br /> <br /> Lung Cancer Detection by an Electronic Nose<br /> <br /> === Project description ===<br /> <br /> The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification process based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. In our work, we used an array composed of six Metal Oxide Semiconductor (MOS) sensors.<br /> In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method. <br /> <br /> During a first pilot study of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the<br /> olfactory signal associated to exhalations of subjects. Results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from<br /> different types of lung cancer (primary and not) at different stages.<br /> In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ at best, and to reduce the dimensionality<br /> of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric<br /> Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based<br /> on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers<br /> and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation. <br /> <br /> The achieved satisfactory results pushed us to begin a new study, in roder to confirm the obtained promising results and to evaluate the ripetibility of our results. We analyzed 104 breath samples of 52 subjects, 22 healthy subjects and 30 subjects with primary lung cancer at different stages. The acquisition has been done inviting subjects to breath into a nalophan bag, later input into the electronic nose. In order to find the best statistical model able to discriminate between the two classes ‘healthy’ and ‘lung cancer’ subjects, and to reduce the dimensionality of the problem, we implemented a genetic algorithm (GA) that found the best combination of feature selection, feature projection and classifier. In particular, according to the feature selection issue, we considered methods based on exponential, sequential and randomized algorithms. Principal Component Analysis (PCA), Fisher’s Linear Discriminant Analysis (LDA) and Non Parametric Linear Discriminant Analysis (NPLDA) have been considered to project features into a lower dimensional space. Classification has been performed implementing several supervised pattern classification algorithms, based on different k-nearest neighbors (k-NN) approaches (classic, modified and fuzzy k-NN), on linear and quadratic discriminant classifiers and on a feed-forward artificial neural network (ANN). The best solution provided from the genetic algorithm, has been the projection of the found subset of features into a single component using the Fisher’s Linear Discriminant Analysis (LDA) and a classification based on the k-Nearest Neighbours (k-NN) method. Performing a Student’s t-test between all pair of considered models, no significative differences emerged, suggesting that all computational intelligence methods that we have applied provided satisfying results. The observed results, all validated using cross-validation, have been very satisfactory achieving an average accuracy of 96.2%, an average sensitivity of 93.3% and an average specificity of 100%, as well as very small confidence intervals. These results confirmed a previous pilot study where we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5% (on 58 control subjects and 43 lung cancer subjects). We also investigated the possibility of performing early diagnosis, building a model able to predict a sample belonging to a subject with primary lung cancer at stage I, compared to healthy subjects. Also in this analysis results have been excellent, achieving an average accuracy of 92.85%, an average sensitivity of 75.5% and an average specificity of 97.72%. <br /> <br /> The research demonstrate that an instrument as the electronic nose, combined with the appropriate artificial intelligence techniques, is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. Moreover, the instrument is completely non invasive. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.<br /> <br /> === Dates ===<br /> Start date: 2007/01/01<br /> <br /> End date: --<br /> <br /> === Website(s) ===<br /> <br /> At the moment no website avaible<br /> <br /> === People involved ===<br /> <br /> ===== Project head(s) =====<br /> <br /> A. Bonarini - [[User:AndreaBonarini]]<br /> <br /> M. Matteucci - [[User:MatteoMatteucci]]<br /> <br /> ===== Other Politecnico di Milano people =====<br /> <br /> R. Blatt - [[User:RossellaBlatt]]<br /> <br /> ===== Students currently working on the project =====<br /> <br /> Claudio Trameri - [[User:ClaudioTrameri]]<br /> <br /> Mauro Verdirosa - [[User:MauroVerdirosa]]<br /> <br /> ===== Students who worked on the project in the past =====<br /> <br /> ===== External personnel: =====<br /> <br /> Dott. Ugo Pastorino (Istituto dei Tumori - Milano)<br /> <br /> Dott. Elisa Calabrò (Istituto dei Tumori - Milano)<br /> <br /> Dott. Matteo Della Torre (SACMI - Imola)<br /> <br /> === Laboratory work and risk analysis ===<br /> <br /> Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done. <br /> For this kind of work, there are not potential risks.<br /> <br /> == '''Part 2: project description''' ==<br /> <br /> === State of the art ===<br /> <br /> === Preliminary and sketches ===<br /> <br /> === Design notes and guidelines ===<br /> <br /> === Link to project documents and files ===<br /> <br /> Results obtained from this work have been presented at different conferences:<br /> <br /> * '''Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece''' <br /> :The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th. <br /> :[[Image:PAIS.pdf|Paper-PAIS2008]] <br /> <br /> * '''International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA'''<br /> :'''Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: [[Image:IJCNNfinal.pdf|Paper-IJCNN2007]] <br /> <br /> :Short presentation of the ''Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors'' paper: [[Image:LungCancerIdentificationIJCNN2007.pdf|Presentation-IJCNN2007]]<br /> <br /> * '''International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy'''<br /> <br /> : '''Fuzzy k-NN Lung Cancer Identification by an Electronic Nose''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.<br /> <br /> === Description and results of experiments ===<br /> <br /> === Photos and videos ===<br /> <br /> === Link to source code of the software written for the project ===<br /> <br /> === Description and results of experiments ===<br /> <br /> === Useful internet links ===</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Lung_Cancer_Detection_by_an_Electronic_Nose&diff=5150 2009-02-10T16:17:08Z

<p>FabioBeltramini: /* Project short description */</p> <hr /> <div>== '''Part 1: project profile''' ==<br /> <br /> === Project name ===<br /> <br /> Lung Cancer Detection by an Electronic Nose<br /> <br /> === Project short description ===<br /> <br /> The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification process based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. In our work, we used an array composed of six Metal Oxide Semiconductor (MOS) sensors.<br /> In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method. <br /> <br /> During a first pilot study of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the<br /> olfactory signal associated to exhalations of subjects. Results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from<br /> different types of lung cancer (primary and not) at different stages.<br /> In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ at best, and to reduce the dimensionality<br /> of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric<br /> Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based<br /> on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers<br /> and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation. <br /> <br /> The achieved satisfactory results pushed us to begin a new study, in roder to confirm the obtained promising results and to evaluate the ripetibility of our results. We analyzed 104 breath samples of 52 subjects, 22 healthy subjects and 30 subjects with primary lung cancer at different stages. The acquisition has been done inviting subjects to breath into a nalophan bag, later input into the electronic nose. In order to find the best statistical model able to discriminate between the two classes ‘healthy’ and ‘lung cancer’ subjects, and to reduce the dimensionality of the problem, we implemented a genetic algorithm (GA) that found the best combination of feature selection, feature projection and classifier. In particular, according to the feature selection issue, we considered methods based on exponential, sequential and randomized algorithms. Principal Component Analysis (PCA), Fisher’s Linear Discriminant Analysis (LDA) and Non Parametric Linear Discriminant Analysis (NPLDA) have been considered to project features into a lower dimensional space. Classification has been performed implementing several supervised pattern classification algorithms, based on different k-nearest neighbors (k-NN) approaches (classic, modified and fuzzy k-NN), on linear and quadratic discriminant classifiers and on a feed-forward artificial neural network (ANN). The best solution provided from the genetic algorithm, has been the projection of the found subset of features into a single component using the Fisher’s Linear Discriminant Analysis (LDA) and a classification based on the k-Nearest Neighbours (k-NN) method. Performing a Student’s t-test between all pair of considered models, no significative differences emerged, suggesting that all computational intelligence methods that we have applied provided satisfying results. The observed results, all validated using cross-validation, have been very satisfactory achieving an average accuracy of 96.2%, an average sensitivity of 93.3% and an average specificity of 100%, as well as very small confidence intervals. These results confirmed a previous pilot study where we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5% (on 58 control subjects and 43 lung cancer subjects). We also investigated the possibility of performing early diagnosis, building a model able to predict a sample belonging to a subject with primary lung cancer at stage I, compared to healthy subjects. Also in this analysis results have been excellent, achieving an average accuracy of 92.85%, an average sensitivity of 75.5% and an average specificity of 97.72%. <br /> <br /> The research demonstrate that an instrument as the electronic nose, combined with the appropriate artificial intelligence techniques, is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. Moreover, the instrument is completely non invasive. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.<br /> <br /> === Dates ===<br /> Start date: 2007/01/01<br /> <br /> End date: --<br /> <br /> === Website(s) ===<br /> <br /> At the moment no website avaible<br /> <br /> === People involved ===<br /> <br /> ===== Project head(s) =====<br /> <br /> A. Bonarini - [[User:AndreaBonarini]]<br /> <br /> M. Matteucci - [[User:MatteoMatteucci]]<br /> <br /> ===== Other Politecnico di Milano people =====<br /> <br /> R. Blatt - [[User:RossellaBlatt]]<br /> <br /> ===== Students currently working on the project =====<br /> <br /> Claudio Trameri - [[User:ClaudioTrameri]]<br /> <br /> Mauro Verdirosa - [[User:MauroVerdirosa]]<br /> <br /> ===== Students who worked on the project in the past =====<br /> <br /> ===== External personnel: =====<br /> <br /> Dott. Ugo Pastorino (Istituto dei Tumori - Milano)<br /> <br /> Dott. Elisa Calabrò (Istituto dei Tumori - Milano)<br /> <br /> Dott. Matteo Della Torre (SACMI - Imola)<br /> <br /> === Laboratory work and risk analysis ===<br /> <br /> Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done. <br /> For this kind of work, there are not potential risks.<br /> <br /> == '''Part 2: project description''' ==<br /> <br /> === State of the art ===<br /> <br /> === Preliminary and sketches ===<br /> <br /> === Design notes and guidelines ===<br /> <br /> === Link to project documents and files ===<br /> <br /> Results obtained from this work have been presented at different conferences:<br /> <br /> * '''Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece''' <br /> :The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th. <br /> :[[Image:PAIS.pdf|Paper-PAIS2008]] <br /> <br /> * '''International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA'''<br /> :'''Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: [[Image:IJCNNfinal.pdf|Paper-IJCNN2007]] <br /> <br /> :Short presentation of the ''Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors'' paper: [[Image:LungCancerIdentificationIJCNN2007.pdf|Presentation-IJCNN2007]]<br /> <br /> * '''International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy'''<br /> <br /> : '''Fuzzy k-NN Lung Cancer Identification by an Electronic Nose''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.<br /> <br /> === Description and results of experiments ===<br /> <br /> === Photos and videos ===<br /> <br /> === Link to source code of the software written for the project ===<br /> <br /> === Description and results of experiments ===<br /> <br /> === Useful internet links ===</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Camcorder&diff=5149 2009-02-10T15:15:34Z

<p>FabioBeltramini: /* Where is the camera now */</p> <hr /> <div>In the AIRLab you can find also a ''memory camcorder'', i.e. a consumer camera system that records (lossy) compressed digital video onto standard SD flash memory cards. It's a [http://www.samsung.com/uk/support/download/supportDown.do?group=homeentertainment&amp;type=camcorder&amp;subtype=flashcamcorder&amp;model_nm=VP-MX10&amp;prd_ia_cd=03110800&amp;disp_nm=VP-MX10&amp;mType=&amp;dType=D&amp;vType=R Samsung - VP-MX10H] and can be used to record on video demos, lessons or talks. It is fitted with an 8GB SD card, i.e. the biggest it can be used with.<br /> <br /> Main features:<br /> * maximum resolution 720x576 pixel (progressive scan), the same as DVD<br /> * MPEG4 encoding (Mplayer for Linux, for example, plays it back perfectly)<br /> * 2.7&quot; 16:9 LCD Display (but recorded image format is 4:3)<br /> * 34x Optical zoom (1200x with digital zoom)<br /> * image stabilizer (use it if you go over, say, 4x of zoom factor)<br /> * USB connection to PC (it's seen as a mass storage device)<br /> * maximum usable SD card capacity 8GB<br /> * 220' of video on an 8GB card in best-quality mode<br /> * 120' (claimed) battery duration (with fully charged battery, during recording and if you don't play much with the zoom)<br /> * unreliable battery charge indicator :-(<br /> * battery discharges quickly even when the device is off, so be sure to recharge it before use<br /> <br /> ===Where is the camera now===<br /> The camera is in [[User:FabioBeltramini|Fabio Beltramini]]'s hands from February 10th.<br /> <br /> == Booking ==<br /> <br /> If you want to use the camera, please book in advance by adding an entry to the table.<br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || [[User:DonaldDuck]]<br /> |- <br /> | Friday 15 April || 9:30-13:00 || [[User:MickeyMouse]]<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> <br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person<br /> |-<br /> |10 Feb || 9:30-15:00 || [[User:BernardoDalSeno]]<br /> |-<br /> |}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=IIT-Lab&diff=4989 2009-01-20T23:18:17Z

<p>FabioBeltramini: </p> <hr /> <div>== What is the IIT-Lab ==<br /> <br /> AIRLab-IITLab is dedicated to activities founded by the Italian Institute of Technology. <br /> The lab hosts activities related to Brain-Computer Interfaces (BCI) and Affective Computing.<br /> <br /> === Location ===<br /> It is located in the Rimembranze di Lambrate building of the Department of Electronics and Information, Via Rimembranze di Lambrate, 14, Milan. <br /> <br /> === Access Rules ===<br /> The access to AIRLab-IITLab is reserved to registered users. If you are student and want to register, you have to fill the AIRLab registration form (to be signed by your tutor) and the security form. The key of the lab is provided to registered users by the doorkeeper at the main entrance of the Lambrate building. <br /> <br /> === Booking ===<br /> <br /> Please book the instrument you want to use by adding an entry to the table; the booking of an instrument implies the booking of the room. If you want to use a different instrument at the same time of an existing booking, please contact the other person involved and check that you can share the room; alternatively, you can ask the doorkeeper for an empty room in the building.<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || [[User:DonaldDuck]] || ProComp<br /> |- <br /> | Friday 15 April || 9:30-13:00 || [[User:MickeyMouse]] || EEG<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person !! Instrument<br /> |-<br /> 22 Jan || 15:00-18:00 || Fabio Beltr. || EEG<br /> |-<br /> |}<br /> <br /> == Links ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[Affective Computing]] page on this Wiki<br /> * [http://www.airlab.elet.polimi.it/index.php/airlab/visitor_info/airlab_iitlab AIRLab - IITLab]</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=IIT-Lab&diff=4988 2009-01-20T23:18:02Z

<p>FabioBeltramini: </p> <hr /> <div>== What is the IIT-Lab ==<br /> <br /> AIRLab-IITLab is dedicated to activities founded by the Italian Institute of Technology. <br /> The lab hosts activities related to Brain-Computer Interfaces (BCI) and Affective Computing.<br /> <br /> === Location ===<br /> It is located in the Rimembranze di Lambrate building of the Department of Electronics and Information, Via Rimembranze di Lambrate, 14, Milan. <br /> <br /> === Access Rules ===<br /> The access to AIRLab-IITLab is reserved to registered users. If you are student and want to register, you have to fill the AIRLab registration form (to be signed by your tutor) and the security form. The key of the lab is provided to registered users by the doorkeeper at the main entrance of the Lambrate building. <br /> <br /> === Booking ===<br /> <br /> Please book the instrument you want to use by adding an entry to the table; the booking of an instrument implies the booking of the room. If you want to use a different instrument at the same time of an existing booking, please contact the other person involved and check that you can share the room; alternatively, you can ask the doorkeeper for an empty room in the building.<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || [[User:DonaldDuck]] || ProComp<br /> |- <br /> | Friday 15 April || 9:30-13:00 || [[User:MickeyMouse]] || EEG<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person !! Instrument !<br /> |-<br /> 22 Jan || 15:00-18:00 || Fabio Beltr. || EEG<br /> |-<br /> |}<br /> <br /> == Links ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[Affective Computing]] page on this Wiki<br /> * [http://www.airlab.elet.polimi.it/index.php/airlab/visitor_info/airlab_iitlab AIRLab - IITLab]</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=IIT-Lab&diff=4987 2009-01-20T23:17:39Z

<p>FabioBeltramini: </p> <hr /> <div>== What is the IIT-Lab ==<br /> <br /> AIRLab-IITLab is dedicated to activities founded by the Italian Institute of Technology. <br /> The lab hosts activities related to Brain-Computer Interfaces (BCI) and Affective Computing.<br /> <br /> === Location ===<br /> It is located in the Rimembranze di Lambrate building of the Department of Electronics and Information, Via Rimembranze di Lambrate, 14, Milan. <br /> <br /> === Access Rules ===<br /> The access to AIRLab-IITLab is reserved to registered users. If you are student and want to register, you have to fill the AIRLab registration form (to be signed by your tutor) and the security form. The key of the lab is provided to registered users by the doorkeeper at the main entrance of the Lambrate building. <br /> <br /> === Booking ===<br /> <br /> Please book the instrument you want to use by adding an entry to the table; the booking of an instrument implies the booking of the room. If you want to use a different instrument at the same time of an existing booking, please contact the other person involved and check that you can share the room; alternatively, you can ask the doorkeeper for an empty room in the building.<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || [[User:DonaldDuck]] || ProComp<br /> |- <br /> | Friday 15 April || 9:30-13:00 || [[User:MickeyMouse]] || EEG<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person !! Instrument<br /> |-<br /> 22 Jan || 15:00-18:00 || Fabio Beltr. || EEG<br /> |-<br /> |}<br /> <br /> == Links ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[Affective Computing]] page on this Wiki<br /> * [http://www.airlab.elet.polimi.it/index.php/airlab/visitor_info/airlab_iitlab AIRLab - IITLab]</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=IIT-Lab&diff=4986 2009-01-20T23:17:04Z

<p>FabioBeltramini: </p> <hr /> <div>== What is the IIT-Lab ==<br /> <br /> AIRLab-IITLab is dedicated to activities founded by the Italian Institute of Technology. <br /> The lab hosts activities related to Brain-Computer Interfaces (BCI) and Affective Computing.<br /> <br /> === Location ===<br /> It is located in the Rimembranze di Lambrate building of the Department of Electronics and Information, Via Rimembranze di Lambrate, 14, Milan. <br /> <br /> === Access Rules ===<br /> The access to AIRLab-IITLab is reserved to registered users. If you are student and want to register, you have to fill the AIRLab registration form (to be signed by your tutor) and the security form. The key of the lab is provided to registered users by the doorkeeper at the main entrance of the Lambrate building. <br /> <br /> === Booking ===<br /> <br /> Please book the instrument you want to use by adding an entry to the table; the booking of an instrument implies the booking of the room. If you want to use a different instrument at the same time of an existing booking, please contact the other person involved and check that you can share the room; alternatively, you can ask the doorkeeper for an empty room in the building.<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || [[User:DonaldDuck]] || ProComp<br /> |- <br /> | Friday 15 April || 9:30-13:00 || [[User:MickeyMouse]] || EEG<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person !! Instrument<br /> |- 22 Jan || 15:00-18:00 || Fabio Beltr. || EEG<br /> |-<br /> |}<br /> <br /> == Links ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[Affective Computing]] page on this Wiki<br /> * [http://www.airlab.elet.polimi.it/index.php/airlab/visitor_info/airlab_iitlab AIRLab - IITLab]</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=IIT-Lab&diff=4985 2009-01-20T23:16:39Z

<p>FabioBeltramini: </p> <hr /> <div>== What is the IIT-Lab ==<br /> <br /> AIRLab-IITLab is dedicated to activities founded by the Italian Institute of Technology. <br /> The lab hosts activities related to Brain-Computer Interfaces (BCI) and Affective Computing.<br /> <br /> === Location ===<br /> It is located in the Rimembranze di Lambrate building of the Department of Electronics and Information, Via Rimembranze di Lambrate, 14, Milan. <br /> <br /> === Access Rules ===<br /> The access to AIRLab-IITLab is reserved to registered users. If you are student and want to register, you have to fill the AIRLab registration form (to be signed by your tutor) and the security form. The key of the lab is provided to registered users by the doorkeeper at the main entrance of the Lambrate building. <br /> <br /> === Booking ===<br /> <br /> Please book the instrument you want to use by adding an entry to the table; the booking of an instrument implies the booking of the room. If you want to use a different instrument at the same time of an existing booking, please contact the other person involved and check that you can share the room; alternatively, you can ask the doorkeeper for an empty room in the building.<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || [[User:DonaldDuck]] || ProComp<br /> |- <br /> | Friday 15 April || 9:30-13:00 || [[User:MickeyMouse]] || EEG<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person !! Instrument<br /> | 22 Jan || 15:00-18:00 || Fabio Beltr. || EEG<br /> |-<br /> |}<br /> <br /> == Links ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[Affective Computing]] page on this Wiki<br /> * [http://www.airlab.elet.polimi.it/index.php/airlab/visitor_info/airlab_iitlab AIRLab - IITLab]</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Ocular_Artifacts_Filter_implementation_for_a_BCI_based_on_motor_imagery&diff=4573 2008-10-22T13:11:47Z

<p>FabioBeltramini: </p> <hr /> <div>== '''Part 1: Project profile''' ==<br /> <br /> === Project name ===<br /> <br /> Implementazione di un filtro per la rimozione degli artefatti oculari per BCI basate su motor imagery.<br /> <br /> === Project short description ===<br /> <br /> La presenza di artefatti nel segnale è una delle problematiche alle quali bisogna prestare particolare attenzione quando si progetta/realizza un sistema BCI. Lo scopo del progetto è quello di realizzare un modulo di filtraggio basato su rejection degli artefatti oculari (EOG) per l'applicazione BCI2000.<br /> <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:FabioBeltramini | Fabio Beltramini]] <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 /> === '''Artefatti''' ===<br /> Gli artefatti sono segnali indesiderati che possono influenzare anche pesantemente il segnale cerebrale registrato con un elettroencefalografo. Si distinguono in due categorie principali in base alla loro origine: fisiologica o no. Sono esempi di artefatti non fisiologici i disturbi introdotti dalla rete elettrica (50/60 Hz), dai cambiamenti di impedenza agli elettrodi, e più in generale tutti i disturbi esterni. Si classificano, invece, come fisiologici gli artefatti causati dal movimento degli occhi (electrooculography EOG) o da altri muscoli del soggetto (electromyography EMG). <br /> In genere, tuttavia, gli artefatti di tipo non fisiologico possono essere rimossi dal segnale attenendosi a semplici precauzioni in fase di registrazione. Non è così per quelli di tipo fisiologico che necessitano, invece, di essere presi in considerazione nella progettazione di un sistema BCI, sviluppando tecniche e algoritmi per la loro riduzione o completa rimozione.<br /> <br /> === '''Algoritmo implementato''' ===<br /> Per questo progetto si è scelto di implementare l'algoritmo proposto da Sanjay Gupta e Harvinder Singh (vedi Rif.) il cui principio di funzionamento è descritto dal seguente schema a blocchi:<br /> <br /> [[Image:EOGFilter.GIF]]<br /> <br /> <br /> Il segnale, dopo essere stato filtrato da un opportuno filtro FIR, viene confrontato in real-time con una soglia (diversa da soggetto a soggetto e da sessione a sessione). Al verificarsi del superamento della soglia il segnale viene &quot;pulito&quot; eliminando gli N/2 campioni prima dell'istante in cui avviene il superamento della soglia e N/2 campioni dopo. Il gap introdotto viene riempito shiftando a destra N/2 campioni nuovi.<br /> <br /> == '''Part 3: References''' ==<br /> <br /> * S. Gupta, H. Singh ''&quot;Preprocessing EEG Signals for Direct Human-System Interface&quot;'' IEEE, 1996 pp. 32-37<br /> * R.J. Croft, R.J. Barry ''&quot;Removal of ocular artifact from the EEG: a review&quot;'' Neurophysiol Clin, 2000; 30: 5-19<br /> <br /> == '''Part 4: Links''' ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[Electroencephalographs]]<br /> * [[How to mount electrodes]]<br /> * [[How to setup BCI software]]</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=File:EOGFilter.GIF&diff=4572 2008-10-22T13:11:15Z

<p>FabioBeltramini: </p> <hr /> <div></div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Ocular_Artifacts_Filter_implementation_for_a_BCI_based_on_motor_imagery&diff=4571 2008-10-22T13:10:41Z

<p>FabioBeltramini: </p> <hr /> <div>== '''Part 1: Project profile''' ==<br /> <br /> === Project name ===<br /> <br /> Implementazione di un filtro per la rimozione degli artefatti oculari per BCI basate su motor imagery.<br /> <br /> === Project short description ===<br /> <br /> La presenza di artefatti nel segnale è una delle problematiche alle quali bisogna prestare particolare attenzione quando si progetta/realizza un sistema BCI. Lo scopo del progetto è quello di realizzare un modulo di filtraggio basato su rejection degli artefatti oculari (EOG) per l'applicazione BCI2000.<br /> <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:FabioBeltramini | Fabio Beltramini]] <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 /> === '''Artefatti''' ===<br /> Gli artefatti sono segnali indesiderati che possono influenzare anche pesantemente il segnale cerebrale registrato con un elettroencefalografo. Si distinguono in due categorie principali in base alla loro origine: fisiologica o no. Sono esempi di artefatti non fisiologici i disturbi introdotti dalla rete elettrica (50/60 Hz), dai cambiamenti di impedenza agli elettrodi, e più in generale tutti i disturbi esterni. Si classificano, invece, come fisiologici gli artefatti causati dal movimento degli occhi (electrooculography EOG) o da altri muscoli del soggetto (electromyography EMG). <br /> In genere, tuttavia, gli artefatti di tipo non fisiologico possono essere rimossi dal segnale attenendosi a semplici precauzioni in fase di registrazione. Non è così per quelli di tipo fisiologico che necessitano, invece, di essere presi in considerazione nella progettazione di un sistema BCI, sviluppando tecniche e algoritmi per la loro riduzione o completa rimozione.<br /> <br /> === '''Algoritmo implementato''' ===<br /> Per questo progetto si è scelto di implementare l'algoritmo proposto da Sanjay Gupta e Harvinder Singh (vedi Rif.) il cui principio di funzionamento è descritto dal seguente schema a blocchi:<br /> <br /> [[Image:EOGFilter.jpg]]<br /> <br /> <br /> Il segnale, dopo essere stato filtrato da un opportuno filtro FIR, viene confrontato in real-time con una soglia (diversa da soggetto a soggetto e da sessione a sessione). Al verificarsi del superamento della soglia il segnale viene &quot;pulito&quot; eliminando gli N/2 campioni prima dell'istante in cui avviene il superamento della soglia e N/2 campioni dopo. Il gap introdotto viene riempito shiftando a destra N/2 campioni nuovi.<br /> <br /> == '''Part 3: References''' ==<br /> <br /> * S. Gupta, H. Singh ''&quot;Preprocessing EEG Signals for Direct Human-System Interface&quot;'' IEEE, 1996 pp. 32-37<br /> * R.J. Croft, R.J. Barry ''&quot;Removal of ocular artifact from the EEG: a review&quot;'' Neurophysiol Clin, 2000; 30: 5-19<br /> <br /> == '''Part 4: Links''' ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[Electroencephalographs]]<br /> * [[How to mount electrodes]]<br /> * [[How to setup BCI software]]</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Ocular_Artifacts_Filter_implementation_for_a_BCI_based_on_motor_imagery&diff=4570 2008-10-22T12:46:05Z

<p>FabioBeltramini: </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:FabioBeltramini | Fabio Beltramini]] <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 /> <br /> == '''Part 4: References''' ==<br /> <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>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Ocular_Artifacts_Filter_implementation_for_a_BCI_based_on_motor_imagery&diff=4569 2008-10-22T12:45:22Z

<p>FabioBeltramini: New page: == '''Part 1: Project profile''' == === Project name === BCI based on Motor Imagery === Project short description === This project is aimed is to control an external device through the...</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:Fabio Beltramini | Fabio Beltramini]] <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 /> <br /> == '''Part 4: References''' ==<br /> <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>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=4568 2008-10-22T12:41:57Z

<p>FabioBeltramini: </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 /> <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 /> <br /> == '''Part 3: 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>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=4567 2008-10-22T12:41:37Z

<p>FabioBeltramini: </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 /> <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 /> <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>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Projects&diff=4566 2008-10-22T12:40:44Z

<p>FabioBeltramini: </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 /> <br /> * [[Multiagent cooperation|Multiagent cooperating system]]<br /> <br /> * [[Planning in Ambient Intelligence scenarios| Planning in Ambient Intelligence scenarios]]<br /> <br /> * [[Game theoretic analysis of electric power| Game theoretic analysis of electric power market]]<br /> <br /> ==== [[BioSignal Analysis]] ====<br /> ----<br /> ====== [[Affective Computing]] ======<br /> <br /> * [[Relatioship between Cognition and Emotion in Rehabilitation Robotics]]<br /> * [[Driving companions]]<br /> * [[Emotion from Interaction]]<br /> * [[Affective Devices]]<br /> <br /> ====== [[Brain-Computer Interface]] ======<br /> <br /> * [[Online P300 and ErrP recognition with BCI2000]]<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 /> ** [[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 /> * [[Mu and beta rhythm-based BCI]]<br /> <br /> ====== [[Automatic Detection Of Sleep Stages]] ======<br /> <br /> * [[Sleep Staging with HMM]]<br /> <br /> ====== [[Analysis of the Olfactory Signal]] ======<br /> <br /> * [[Lung Cancer Detection by an Electronic Nose]]<br /> * [[HE-KNOWS - An electronic nose]]<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 /> * [[Image retargeting by k-seam removal]]<br /> <br /> * [[Particle filter for object tracking]]<br /> <br /> * [[Template based paper like reconstruction when the edges are straight]]<br /> <br /> * [[Wii Remote headtracking and active projector]]<br /> <br /> * [[Vision module for the Milan Robocup Team]]<br /> <br /> * [[Long Exposure Images for Resource-constrained video surveillance]]<br /> <br /> * [[NonPhotorealistic rendering of speed lines]].<br /> <br /> * [[Restoration of blurred objects using cues from the alpha matte]]<br /> <br /> * [[Analyzing Traffic Speed From a Single Night Image - Light Streaks Detection]]<br /> <br /> * [[Plate detection algorithm]]<br /> <br /> * [[A vision-based 3D input device for space curves]]<br /> <br /> * [[Correlation-based 3D reconstruction with pan/tilt stereo-camera]]<br /> <br /> * [[Inverse scaling parametrization for Monocular Simultaneous Localization and Mapping]]<br /> <br /> * [[Image resize by solving a sparse linear system]]<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 /> <br /> * [[B-Smart Behaviour Sequence Modeler and Recognition tool|B-Smart Behaviour Sequence Modeler and Recognition tool]]<br /> <br /> * [[Player modeling in TORCS exploiting SVMs and GPUs parallelism|Player modeling in TORCS exploiting SVMs and GPUs parallelism]]<br /> <br /> * [[Development an Artificial Intelligence System solving the MS Pac-Man videogame |Development an Artificial Intelligence System solving the MS Pac-Man videogame ]]<br /> <br /> * [[Parameters optimization in TORCS exploiting genetic algorithms]]<br /> <br /> * [[Neuroevolution in TORCS for evolving interesting and adaptive behaviors]]<br /> <br /> ==== [[Ontologies and Semantic Web]] ====<br /> ----<br /> * [[Extending a wiki with semantic templates]]<br /> * [[GeoOntology|Geographic ontology for a semantic wiki]]<br /> <br /> ==== [[Philosophy of Artificial Intelligence]] ====<br /> ----<br /> ==== [[Robotics]] ====<br /> ----<br /> ===== [[Robot development]] =====<br /> * [[LURCH - The autonomous wheelchair]]<br /> <br /> * [[Balancing robots: Tilty, TiltOne]]<br /> <br /> * [[ Computer controlled Braking on the Alpaca Golf Cart ]]<br /> <br /> * [[ Computer controlled Steering on the Alpaca Golf Cart ]]<br /> <br /> * [[ Development of a neck for umanoid robot ]]<br /> <br /> ===== [[Benchmarking]] =====<br /> <br /> * [[Rawseeds|RAWSEEDS]]<br /> <br /> ===== [[Bio Robotics]] =====<br /> <br /> * [[PoliManus]]<br /> <br /> * [[ZOIDBERG - An autonomous bio-inspired RoboFish]]<br /> <br /> * [[Styx The 6 Whegs Robot]]<br /> <br /> * [[PolyGlove: a body-based haptic interface]]<br /> <br /> * [[ULISSE]]<br /> <br /> * [[PEKeB: a PiezoElectric KeyBoard]]<br /> <br /> * [[Anthropomorphic Robotic Wrist]]<br /> <br /> * [[High-level architecture for the control of humanoid robot]]<br /> <br /> ===== [[Robogames]] =====<br /> <br /> * [[ROBOWII]] <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 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>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Projects&diff=4565 2008-10-22T12:40:23Z

<p>FabioBeltramini: </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 /> <br /> * [[Multiagent cooperation|Multiagent cooperating system]]<br /> <br /> * [[Planning in Ambient Intelligence scenarios| Planning in Ambient Intelligence scenarios]]<br /> <br /> * [[Game theoretic analysis of electric power| Game theoretic analysis of electric power market]]<br /> <br /> ==== [[BioSignal Analysis]] ====<br /> ----<br /> ====== [[Affective Computing]] ======<br /> <br /> * [[Relatioship between Cognition and Emotion in Rehabilitation Robotics]]<br /> * [[Driving companions]]<br /> * [[Emotion from Interaction]]<br /> * [[Affective Devices]]<br /> <br /> ====== [[Brain-Computer Interface]] ======<br /> <br /> * [[Online P300 and ErrP recognition with BCI2000]]<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 /> ** [[Ocular Artifacts Filter implementation for a BCI based on motor imagery]] (First Level thesis, Francesco Amenta)<br /> * [[Graphical user interface for an autonomous wheelchair]]<br /> * [[Mu and beta rhythm-based BCI]]<br /> <br /> ====== [[Automatic Detection Of Sleep Stages]] ======<br /> <br /> * [[Sleep Staging with HMM]]<br /> <br /> ====== [[Analysis of the Olfactory Signal]] ======<br /> <br /> * [[Lung Cancer Detection by an Electronic Nose]]<br /> * [[HE-KNOWS - An electronic nose]]<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 /> * [[Image retargeting by k-seam removal]]<br /> <br /> * [[Particle filter for object tracking]]<br /> <br /> * [[Template based paper like reconstruction when the edges are straight]]<br /> <br /> * [[Wii Remote headtracking and active projector]]<br /> <br /> * [[Vision module for the Milan Robocup Team]]<br /> <br /> * [[Long Exposure Images for Resource-constrained video surveillance]]<br /> <br /> * [[NonPhotorealistic rendering of speed lines]].<br /> <br /> * [[Restoration of blurred objects using cues from the alpha matte]]<br /> <br /> * [[Analyzing Traffic Speed From a Single Night Image - Light Streaks Detection]]<br /> <br /> * [[Plate detection algorithm]]<br /> <br /> * [[A vision-based 3D input device for space curves]]<br /> <br /> * [[Correlation-based 3D reconstruction with pan/tilt stereo-camera]]<br /> <br /> * [[Inverse scaling parametrization for Monocular Simultaneous Localization and Mapping]]<br /> <br /> * [[Image resize by solving a sparse linear system]]<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 /> <br /> * [[B-Smart Behaviour Sequence Modeler and Recognition tool|B-Smart Behaviour Sequence Modeler and Recognition tool]]<br /> <br /> * [[Player modeling in TORCS exploiting SVMs and GPUs parallelism|Player modeling in TORCS exploiting SVMs and GPUs parallelism]]<br /> <br /> * [[Development an Artificial Intelligence System solving the MS Pac-Man videogame |Development an Artificial Intelligence System solving the MS Pac-Man videogame ]]<br /> <br /> * [[Parameters optimization in TORCS exploiting genetic algorithms]]<br /> <br /> * [[Neuroevolution in TORCS for evolving interesting and adaptive behaviors]]<br /> <br /> ==== [[Ontologies and Semantic Web]] ====<br /> ----<br /> * [[Extending a wiki with semantic templates]]<br /> * [[GeoOntology|Geographic ontology for a semantic wiki]]<br /> <br /> ==== [[Philosophy of Artificial Intelligence]] ====<br /> ----<br /> ==== [[Robotics]] ====<br /> ----<br /> ===== [[Robot development]] =====<br /> * [[LURCH - The autonomous wheelchair]]<br /> <br /> * [[Balancing robots: Tilty, TiltOne]]<br /> <br /> * [[ Computer controlled Braking on the Alpaca Golf Cart ]]<br /> <br /> * [[ Computer controlled Steering on the Alpaca Golf Cart ]]<br /> <br /> * [[ Development of a neck for umanoid robot ]]<br /> <br /> ===== [[Benchmarking]] =====<br /> <br /> * [[Rawseeds|RAWSEEDS]]<br /> <br /> ===== [[Bio Robotics]] =====<br /> <br /> * [[PoliManus]]<br /> <br /> * [[ZOIDBERG - An autonomous bio-inspired RoboFish]]<br /> <br /> * [[Styx The 6 Whegs Robot]]<br /> <br /> * [[PolyGlove: a body-based haptic interface]]<br /> <br /> * [[ULISSE]]<br /> <br /> * [[PEKeB: a PiezoElectric KeyBoard]]<br /> <br /> * [[Anthropomorphic Robotic Wrist]]<br /> <br /> * [[High-level architecture for the control of humanoid robot]]<br /> <br /> ===== [[Robogames]] =====<br /> <br /> * [[ROBOWII]] <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 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>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=4507 2008-10-17T20:33:07Z

<p>FabioBeltramini: </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 /> <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 /> === How to set up BCI instrumentation ===<br /> <br /> You’ll nedd:<br /> <br /> * Electroencephalograph (with PCMCIA connector)<br /> * Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> * Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> * Conductive gel and/or abrasive electrode-gel<br /> * Tape measure to put in the correct position the electrodes<br /> * Adhesive tape (if using single electrodes to fix them)<br /> * Syringe (if using electrodes cap, to insert the conductive gel into the electrodes hole)<br /> * Fototransistor (if necessary)<br /> <br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal.<br /> Mobile phones can be another source of noise: it is better to switch them off before while performing the acquisition.<br /> In order to acquire the brain signal, it is possible to use an electrodes cap, or a set of separated single electrodes.<br /> <br /> ==== Electrodes Cap ====<br /> <br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> <br /> * measure his head size (from the nasion NS to the inion) this is the 100% of the measure<br /> * 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) .<br /> * fill in the electrodes with conductive gel (use the syringe), take care that hair don’t disturb the skin-electrode contact.<br /> * 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.<br /> * connect the cable to the Electroencephalograph.<br /> <br /> ==== Separeted electrodes ====<br /> <br /> 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:<br /> <br /> [[Image:10-20System.jpg]]<br /> <br /> <br /> Connect the reference electrode to the forehead and the GND electrode to the right earlobe.<br /> 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.<br /> Fill in the electrode with a little bit of conductive gel, and put it on the skin, in the correct position.<br /> Fix the cable with adhesive tape, and connect it to the Electroencephalograph.<br /> <br /> ==== EOG ====<br /> <br /> If you want to eliminate EOG artefacts from your data acquisitions, you would record EOG signal putting 2 more electrodes, near the patient eye.<br /> One electrode just over the eyebrow and one under the eye (REF) , like in the picture:<br /> <br /> [[Image:ElectrodesEyes.png]]<br /> <br /> Connect the EOG cables to the A-Channel of the Electroencephalograph.<br /> <br /> ==== Impedance check ====<br /> <br /> Now you can start Galileo Software and check if the correct values of impedance are measured.<br /> 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.<br /> Values under 10 KOhm are acceptable, values under 5 KOhm are very good.<br /> If your values aren’t under 10KOhm try to decrease them, filling the electrodes with more conductive gel until you reach acceptable values.<br /> <br /> === BCI2000 and Galileo ===<br /> <br /> [http://www.bci2000.org BCI2000] is a general-purpose software system for BCI research developed by the Wadsworth Center of the New York.&lt;br/&gt; <br /> In our project BCI2000 is used for:<br /> * data storage,<br /> * stimulus presentation,<br /> * on-line feedback applications.<br /> <br /> Since our EEG amplifier is not directly supported by BCI2000, data acquisition is performed instead by ''Galileo'' that is the software provided with the EEG amplifier. &lt;br/&gt;<br /> Galileo and BCI2000 communicate by means of two components:<br /> * AirBat: a custom plugin for Galileo that sends the acquired data to GalileoSource through a pipe,<br /> * GalileoSource: the data source seen by the other modules of BCI2000.<br /> <br /> BCI2000 implements a client server architecture. When the server (operat) is launched it starts waiting for all the others modules to connect:<br /> * the data source (in our case it is GalileoSource);<br /> * the filter;<br /> * the application.<br /> When all these components are ready it is possible to start modifying the configuration parameters and finally run the application. &lt;br&gt;<br /> Note that GalileoSource will be ready only when the connection with AirBat is correctly established. &lt;br/&gt;<br /> Thus the correct order in which the components should be launched is the following:<br /> # start operat;<br /> # start GalileoSource;<br /> # start the filter component;<br /> # start the application component;<br /> # from Galileo start a new data acquisition with the AirBat plugin.<br /> <br /> == '''Part 3: Implementation of an EOG artifacts filter''' ==<br /> <br /> ==== Students currently working on the project ====<br /> * [[User:FabioBeltramini | Fabio Beltramini]] (bachelor student)<br /> <br /> ==== Filter algorithm description ====<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.</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Electroencephalographs&diff=3904 2008-08-24T20:32:36Z

<p>FabioBeltramini: </p> <hr /> <div>== BeLight ==<br /> <br /> A brief description of EbNeuro BeLight should go here.<br /> <br /> === Connectors ===<br /> * '''1-21:''' EEG inputs<br /> * '''A, B, C, D''' Bipolar inputs<br /> * '''NE:''' EEG reference<br /> * '''ISOGN:''' EEG ground (Isolated ground)<br /> * '''22-24:''' Poly channels<br /> * '''NEP:''' Poly reference<br /> <br /> === Booking ===<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || Donald Duck<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> <br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person<br /> |-<br /> | Mond 25 August || 10:30-19:00 || Rossella<br /> |-<br /> | Wednesday 27 August || 10:00-19:00 || Bernardo<br /> |-<br /> | Thursday 28 August || 10:00-19:00 || Bernardo<br /> |-<br /> |}<br /> <br /> === Electrodes ===<br /> <br /> There are two types of electrodes: electrodes pre-mounted on caps, and single electrodes. They are very simple, but as they are very important for the quality of the signal and are delicate (and also incredibly expensive, btw), please treat them carefully (avoid rattling them against each other, for example).<br /> <br /> ==== Maintenance ====<br /> <br /> The paste or gel used during acquisitions must be thoroughly removed after use. Please follow the following procedure:<br /> # Rinse the electrodes under running water. This step can remove only a part of the gel/paste. Please avoid washing or wetting the plugs.<br /> # Put the electrodes in a container half-filled with water (there is a suitable container in the cabinet of the IITLab), so that the electrodes are completely submerged. Leave them there for some minutes, until all the gel/paste become dissolved. Change the water if it becomes too murky. Do not leave the electrodes underwater for too long.<br /> # Rinse the electrodes under running water (and also rinse the container).<br /> # Put the electrodes in a dry place, where they can dry up. You can accelerate this phase by first patting them with a towel. Again, make sure that plugs don't come in contact with water; if you hang a cap, make sure that water cannot drip along the cable onto the plug.<br /> # Put the electrodes away in their bag in their box. Please make sure that they are absolutely dry before you put them away; the coating of electrodes is rather delicate.<br /> <br /> Please take into account also the time for electrode cleaning when you plan your EEG acquisitions.<br /> <br /> == Presets ==<br /> <br /> Description of the presets saved in the Galileo software.<br /> <br /> ===P300===<br /> Used for P300 and ErrP recordings. There are 4 EEG channels, 1 EOG, 2 external signals in DC; sampling frequency is 512 Hz.<br /> ;Channels and connectors:<br /> :'''EOG''': EOG, input &quot;A&quot;; the &quot;+&quot; input for the electrode above the eye, the &quot;-&quot; input for the one below<br /> :'''Fz''': Fz, input 7<br /> :'''Cz''': Cz, input 12<br /> :'''Pz''': Pz, input 17<br /> :'''Oz''': Oz, input 20 (labeled as &quot;O1&quot; on the amplifier)<br /> :'''Sync''': phototransistor for stimulus synchronization; positive lead (red) in input 22, negative lead (black) in input NEP<br /> :'''Button''': button, used for target signaling; positive lead in input 23, negative lead in input NEP. If it's not used, please short the input 23 with NEP.<br /> <br /> == Links ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[EEG Montage]] for instructions on how to mount electrodes</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Electroencephalographs&diff=3903 2008-08-24T20:32:13Z

<p>FabioBeltramini: </p> <hr /> <div>== BeLight ==<br /> <br /> A brief description of EbNeuro BeLight should go here.<br /> <br /> === Connectors ===<br /> * '''1-21:''' EEG inputs<br /> * '''A, B, C, D''' Bipolar inputs<br /> * '''NE:''' EEG reference<br /> * '''ISOGN:''' EEG ground (Isolated ground)<br /> * '''22-24:''' Poly channels<br /> * '''NEP:''' Poly reference<br /> <br /> === Booking ===<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || Donald Duck<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> <br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person<br /> |-<br /> | Thursday 21 August || 10:00-19:00 || Bernardo <br /> |-<br /> | Wed (Friday?) 22 August || 10:00-14:00 || Rossella <br /> |-<br /> | Mond 25 August || 10:30-19:00 || Rossella<br /> |-<br /> | Wednesday 27 August || 10:00-19:00 || Bernardo<br /> |-<br /> | Thursday 28 August || 10:00-19:00 || Bernardo<br /> |-<br /> |}<br /> <br /> === Electrodes ===<br /> <br /> There are two types of electrodes: electrodes pre-mounted on caps, and single electrodes. They are very simple, but as they are very important for the quality of the signal and are delicate (and also incredibly expensive, btw), please treat them carefully (avoid rattling them against each other, for example).<br /> <br /> ==== Maintenance ====<br /> <br /> The paste or gel used during acquisitions must be thoroughly removed after use. Please follow the following procedure:<br /> # Rinse the electrodes under running water. This step can remove only a part of the gel/paste. Please avoid washing or wetting the plugs.<br /> # Put the electrodes in a container half-filled with water (there is a suitable container in the cabinet of the IITLab), so that the electrodes are completely submerged. Leave them there for some minutes, until all the gel/paste become dissolved. Change the water if it becomes too murky. Do not leave the electrodes underwater for too long.<br /> # Rinse the electrodes under running water (and also rinse the container).<br /> # Put the electrodes in a dry place, where they can dry up. You can accelerate this phase by first patting them with a towel. Again, make sure that plugs don't come in contact with water; if you hang a cap, make sure that water cannot drip along the cable onto the plug.<br /> # Put the electrodes away in their bag in their box. Please make sure that they are absolutely dry before you put them away; the coating of electrodes is rather delicate.<br /> <br /> Please take into account also the time for electrode cleaning when you plan your EEG acquisitions.<br /> <br /> == Presets ==<br /> <br /> Description of the presets saved in the Galileo software.<br /> <br /> ===P300===<br /> Used for P300 and ErrP recordings. There are 4 EEG channels, 1 EOG, 2 external signals in DC; sampling frequency is 512 Hz.<br /> ;Channels and connectors:<br /> :'''EOG''': EOG, input &quot;A&quot;; the &quot;+&quot; input for the electrode above the eye, the &quot;-&quot; input for the one below<br /> :'''Fz''': Fz, input 7<br /> :'''Cz''': Cz, input 12<br /> :'''Pz''': Pz, input 17<br /> :'''Oz''': Oz, input 20 (labeled as &quot;O1&quot; on the amplifier)<br /> :'''Sync''': phototransistor for stimulus synchronization; positive lead (red) in input 22, negative lead (black) in input NEP<br /> :'''Button''': button, used for target signaling; positive lead in input 23, negative lead in input NEP. If it's not used, please short the input 23 with NEP.<br /> <br /> == Links ==<br /> <br /> * [[Brain-Computer Interface]] page on this Wiki<br /> * [[EEG Montage]] for instructions on how to mount electrodes</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Electroencephalographs&diff=3897 2008-08-20T12:34:12Z

<p>FabioBeltramini: </p> <hr /> <div>== BeLight ==<br /> <br /> A brief description of EbNeuro BeLight should go here.<br /> <br /> === Booking ===<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || Donald Duck<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> <br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person<br /> |-<br /> | Thursday 21 August || 10:00-19:00 || Bernardo <br /> |-<br /> | Wed 22 August || 10:00-14:00 || Rossella <br /> |-<br /> |-<br /> |}<br /> <br /> === Electrodes ===<br /> <br /> There are two types of electrodes: electrodes pre-mounted on caps, and single electrodes. They are very simple, but as they are very important for the quality of the signal and are delicate (and also incredibly expensive, btw), please treat them carefully (avoid rattling them against each other, for example).<br /> <br /> ==== Maintenance ====<br /> <br /> The paste or gel used during acquisitions must be thoroughly removed after use. Please follow the following procedure:<br /> # Rinse the electrodes under running water. This step can remove only a part of the gel/paste. Please avoid washing or wetting the plugs.<br /> # Put the electrodes in a container half-filled with water (there is a suitable container in the cabinet of the IITLab), so that the electrodes are completely submerged. Leave them there for some minutes, until all the gel/paste become dissolved. Change the water if it becomes too murky. Do not leave the electrodes underwater for too long.<br /> # Rinse the electrodes under running water (and also rinse the container).<br /> # Put the electrodes in a dry place, where they can dry up. You can accelerate this phase by first patting them with a towel. Again, make sure that plugs don't come in contact with water; if you hang a cap, make sure that water cannot drip along the cable onto the plug.<br /> # Put the electrodes away in their bag in their box. Please make sure that they are absolutely dry before you put them away; the coating of electrodes is rather delicate.<br /> <br /> Please take into account also the time for electrode cleaning when you plan your EEG acquisitions.</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Electroencephalographs&diff=3805 2008-07-17T16:25:41Z

<p>FabioBeltramini: /* Booking */</p> <hr /> <div>== BeLight ==<br /> <br /> A brief description of EbNeuro BeLight should go here.<br /> <br /> === Booking ===<br /> <br /> &lt;!-- Please keep the table lines ordered by time (nearest bookings first); add new entries like this:<br /> ---CUT---<br /> | Monday 13 March || 11:00-18:00 || Donald Duck<br /> |- <br /> ---CUT---<br /> Use abbreviations, if you like.<br /> Please remove old entries.<br /> --&gt;<br /> <br /> {| border=&quot;1&quot;<br /> ! Day !! Time !! Person<br /> |-<br /> | Thursday 17 July || 10:30 - 19:00 || Rossella<br /> |-<br /> | Friday 18 July || 10:30 - 19:00 || Rossella<br /> |-<br /> | Monday 21 July || 10:00-18:00 || Rossella<br /> |-<br /> | Tuesday 22 July || 10:00-18:00 || Bernardo<br /> |-<br /> | Wendsday 23 July || 10:00-18:00 || Rossella<br /> |-<br /> | Thursday 24 July || 10:00-18:00 || Bernardo<br /> |-<br /> | Friday 25 July || 10:00-18:00 || Rossella<br /> |- <br /> | Thursday 31 July || 10:30 - 19:00 || Rossella<br /> |-<br /> | Friday 1 August || 10:30 - 19:00 || Rossella<br /> |-<br /> |}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=BCI_based_on_Motor_Imagery&diff=3090 2008-05-20T22:13:09Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> &lt;br/&gt;&lt;br/&gt;<br /> You’ll nedd:<br /> <br /> - Electroencephalograph (with PCMCIA connector)<br /> <br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> <br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> <br /> - Conductive gel and/or abrasive electrode-gel<br /> <br /> - Tape measure to put in the correct position the electrodes<br /> <br /> - Adhesive tape (if using single electrodes to fix them)<br /> <br /> - Syringe (if using electrodes cap, to insert the conductive gel into the electrodes hole)<br /> <br /> - Fototransistor (if necessary)<br /> <br /> <br /> 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.<br /> <br /> <br /> <br /> In order to acquire the brain signal, it is possible to use an electrodes cap, or a set of separated single electrodes.<br /> <br /> <br /> '''''Electrodes Cap'''''<br /> <br /> <br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> <br /> - measure his head size (from the nasion NS to the inion) this is the 100% of the measure<br /> <br /> - 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) .<br /> <br /> - fill in the electrodes with conductive gel (use the syringe), take care that hair don’t disturb the skin-electrode contact.<br /> <br /> - 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.<br /> <br /> - connect the cable to the Electroencephalograph.<br /> <br /> <br /> <br /> '''''Separeted electrodes'''''<br /> <br /> 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:<br /> <br /> [[Image:10-20System.jpg]]<br /> <br /> <br /> Connect the reference electrode to the forehead and the GND electrode to the right earlobe.<br /> 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.<br /> Fill in the electrode with a little bit of conductive gel, and put it on the skin, in the correct position.<br /> Fix the cable with adhesive tape, and connect it to the Electroencephalograph.<br /> <br /> <br /> <br /> ----<br /> <br /> <br /> If you want to eliminate EOG artefacts from your data acquisitions, you would record EOG signal putting 2 more electrodes, near the patient eye.<br /> One electrode just over the eyebrow and one under the eye (REF) , like in the figure:<br /> <br /> FIGURA elettrodi occhi<br /> <br /> Connect the EOG cables to the A-Channel of the Electroencephalograph.<br /> <br /> Now you can start Galileo Software and check if the correct values of impedance are measured.<br /> 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.<br /> Values under 10 KOhm are acceptable, values under 5 KOhm are very good.<br /> If your values aren’t under 10KOhm try to decrease them, filling the electrodes with more conductive gel until you reach acceptable values.</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=File:10-20System.jpg&diff=2690 2008-04-28T14:12:33Z

<p>FabioBeltramini: </p> <hr /> <div></div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2689 2008-04-28T14:07:22Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> &lt;br&gt;&lt;br&gt;<br /> You’ll nedd:<br /> <br /> - Electroencephalograph (with PCMCIA connector)<br /> <br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> <br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> <br /> - Conductive gel<br /> <br /> - Tape measure<br /> <br /> - Adhesive tape<br /> <br /> - Syringe<br /> <br /> - Cotton fioc<br /> <br /> - Abrasive electrode-gel<br /> <br /> <br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.<br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> <br /> <br /> <br /> '''''Electrodes Cap'''''<br /> <br /> <br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> <br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure<br /> <br /> - now you can put the CZ electrode at 50% of the entire measure, and check the positions of the other electrodes.<br /> <br /> - fill in the electrodes with conductive gel (use the syringe), take care that hair don’t disturb the skin-electrode contact.<br /> <br /> - connect the cable to the Electroencephalograph.<br /> <br /> <br /> <br /> '''''Separeted electrodes'''''<br /> <br /> 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:<br /> <br /> [[Image:10-20System.jpg]]<br /> <br /> <br /> Connect the reference electrode to the forehead and the GND electrode to the right earlobe.<br /> 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.<br /> Fill in the electrode with a little bit of conductive gel, and put it on the skin, in the correct position.<br /> Fix the cable with adhesive tape, and connect it to the Electroencephalograph.<br /> <br /> <br /> <br /> ----<br /> <br /> <br /> If you want to eliminate EOG artefacts from your data acquisitions, you would record EOG signal putting 2 more electrodes, near the patient eye.<br /> One electrode just over the eyebrow and one under the eye (REF) , like in the figure:<br /> <br /> FIGURA elettrodi occhi<br /> <br /> Connect the EOG cables to the A-Channel of the Electroencephalograph.<br /> <br /> Now you can start Galileo Software and check if the correct values of impedance are measured.<br /> 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.<br /> Values under 10 KOhm are acceptable, values under 5 KOhm are very good.<br /> If your values aren’t under 10KOhm try to decrease them, filling the electrodes with more conductive gel until you reach acceptable values.</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2688 2008-04-28T14:06:04Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> &lt;br&gt;&lt;br&gt;<br /> You’ll nedd:<br /> <br /> - Electroencephalograph (with PCMCIA connector)<br /> <br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> <br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> <br /> - Conductive gel<br /> <br /> - Tape measure<br /> <br /> - Adhesive tape<br /> <br /> - Syringe<br /> <br /> - Cotton fioc<br /> <br /> - Abrasive electrode-gel<br /> <br /> <br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.<br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> <br /> <br /> <br /> '''''Electrodes Cap'''''<br /> <br /> <br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> <br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure<br /> <br /> - now you can put the CZ electrode at 50% of the entire measure, and check the positions of the other electrodes.<br /> <br /> - fill in the electrodes with conductive gel (use the syringe), take care that hair don’t disturb the skin-electrode contact.<br /> <br /> - connect the cable to the Electroencephalograph.<br /> <br /> <br /> <br /> '''''Separeted electrodes'''''<br /> <br /> 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:<br /> <br /> <br /> FIGURA 10 20 system<br /> <br /> <br /> <br /> Connect the reference electrode to the forehead and the GND electrode to the right earlobe.<br /> 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.<br /> Fill in the electrode with a little bit of conductive gel, and put it on the skin, in the correct position.<br /> Fix the cable with adhesive tape, and connect it to the Electroencephalograph.<br /> <br /> <br /> <br /> ----<br /> <br /> <br /> If you want to eliminate EOG artefacts from your data acquisitions, you would record EOG signal putting 2 more electrodes, near the patient eye.<br /> One electrode just over the eyebrow and one under the eye (REF) , like in the figure:<br /> <br /> FIGURA elettrodi occhi<br /> <br /> Connect the EOG cables to the A-Channel of the Electroencephalograph.<br /> <br /> Now you can start Galileo Software and check if the correct values of impedance are measured.<br /> 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.<br /> Values under 10 KOhm are acceptable, values under 5 KOhm are very good.<br /> If your values aren’t under 10KOhm try to decrease them, filling the electrodes with more conductive gel until you reach acceptable values.</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2687 2008-04-28T14:01:43Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> &lt;br&gt;&lt;br&gt;<br /> You’ll nedd:<br /> <br /> - Electroencephalograph (with PCMCIA connector)<br /> <br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> <br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> <br /> - Conductive gel<br /> <br /> - Tape measure<br /> <br /> - Adhesive tape<br /> <br /> - Syringe<br /> <br /> - Cotton fioc<br /> <br /> - Abrasive electrode-gel<br /> <br /> <br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.<br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> <br /> <br /> <br /> '''''Electrodes Cap'''''<br /> <br /> <br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> <br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure<br /> <br /> - now you can put the CZ electrode at 50% of the entire measure, and check the positions of the other electrodes.<br /> <br /> - fill in the electrodes with conductive gel (use the syringe), take care that hair don’t disturb the skin-electrode contact.<br /> <br /> - connect the cable to the Electroencephalograph.<br /> <br /> <br /> <br /> '''''Separeted electrodes'''''<br /> <br /> 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:<br /> <br /> <br /> FIGURA 10 20 system<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> <br /> If you want to eliminate EOG artefacts from your data acquisitions, you would record EOG signal putting 2 more electrodes, near the patient eye.<br /> One electrode just over the eyebrow and one under the eye (REF) , like in the figure:<br /> <br /> FIGURA elettrodi occhi<br /> <br /> Connect the EOG cables to the A-Channel of the Electroencephalograph.<br /> <br /> Now you can start Galileo Software and check if the correct values of impedance are measured.<br /> 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.<br /> Values under 10 KOhm are acceptable, values under 5 KOhm are very good.<br /> If your values aren’t under 10KOhm try to decrease them, filling the electrodes with more conductive gel until you reach acceptable values.</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2686 2008-04-28T13:24:34Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> &lt;br&gt;&lt;br&gt;<br /> You’ll nedd:<br /> <br /> - Electroencephalograph (with PCMCIA connector)<br /> <br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> <br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> <br /> - Conductive gel<br /> <br /> - Tape measure<br /> <br /> - Adhesive tape<br /> <br /> <br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.<br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> <br /> <br /> <br /> '''''Electrodes Cap'''''<br /> <br /> <br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> <br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure<br /> <br /> - now you can put the CZ electrode at 50% of the entire measure, and check the positions of the other electrodes.<br /> <br /> - Connect the reference electrode to the forehead and the GND electrode to the right earlobe.<br /> <br /> <br /> '''''Separeted electrodes'''''<br /> <br /> 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:<br /> <br /> <br /> FIGURA 10 20 system<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> <br /> If you want to eliminate EOG artefacts from your data acquisitions, you would record EOG signal putting 2 more electrodes, near the patient eye.<br /> One electrode just over the eyebrow and one under the eye (REF) , like in the figure:<br /> <br /> FIGURA elettrodi occhi</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2682 2008-04-25T10:10:51Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> &lt;br&gt;&lt;br&gt;<br /> You’ll nedd:<br /> <br /> - Electroencephalograph (with PCMCIA connector)<br /> <br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> <br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> <br /> - Conductive gel<br /> <br /> - Tape measure<br /> <br /> - Adhesive tape<br /> <br /> <br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.<br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> <br /> <br /> <br /> '''''Electrodes Cap'''''<br /> <br /> <br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> <br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure<br /> <br /> - now</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2681 2008-04-25T10:08:09Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> &lt;br&gt;&lt;br&gt;<br /> You’ll nedd:<br /> <br /> - Electroencephalograph (with PCMCIA connector)<br /> <br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> <br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> <br /> - Conductive ge<br /> <br /> - Tape measure<br /> <br /> - Adhesive tape<br /> <br /> <br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.<br /> <br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> <br /> <br /> <br /> '''''Electrodes Cap'''''<br /> <br /> <br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> <br /> <br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure<br /> <br /> <br /> - now</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2680 2008-04-25T10:05:40Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> &lt;br&gt;&lt;br&gt;<br /> You’ll nedd: &lt;br&gt;<br /> - Electroencephalograph (with PCMCIA connector)&lt;br&gt;<br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)&lt;br&gt;<br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)&lt;br&gt;<br /> - Conductive gel&lt;br&gt;<br /> - Tape measure&lt;br&gt;<br /> - Adhesive tape&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;br&gt;<br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.&lt;br&gt;<br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> &lt;br&gt;&lt;br&gt;<br /> '''''Electrodes Cap'''''&lt;br&gt;<br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:&lt;br&gt;<br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure&lt;br&gt;<br /> - now</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2679 2008-04-25T10:04:16Z

<p>FabioBeltramini: </p> <hr /> <div>== '''How to set up BCI instruments''' ==<br /> <br /> You’ll nedd: <br /> - Electroencephalograph (with PCMCIA connector)&lt;br&gt;<br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)&lt;br&gt;<br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)&lt;br&gt;<br /> - Conductive gel&lt;br&gt;<br /> - Tape measure&lt;br&gt;<br /> - Adhesive tape&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;br&gt;<br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.&lt;br&gt;<br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> &lt;br&gt;&lt;br&gt;<br /> '''''Electrodes Cap'''''&lt;br&gt;<br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:&lt;br&gt;<br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure&lt;br&gt;<br /> - now</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=Talk:BCI_based_on_Motor_Imagery&diff=2678 2008-04-25T10:02:23Z

<p>FabioBeltramini: </p> <hr /> <div><br /> == '''How to set up BCI instruments''' ==<br /> <br /> <br /> You’ll nedd: <br /> - Electroencephalograph (with PCMCIA connector)<br /> - Laptop (ensures that the AC adapter is NOT connected, for safety reasons)<br /> - Electrodes or an Electrodes Cap (designed for the 10-20 system)<br /> - Conductive gel<br /> - Tape measure<br /> - Adhesive tape<br /> <br /> Before starting, be sure that the Electroencephalograph AC adapter is far away from the patient because 50Hz can interfere and destroy EEG signal. Don’t forget also to switch-off your mobile phone.<br /> Now you can choose if you want to use an electrodes cap, or separated electrodes.<br /> <br /> '''''Electrodes Cap'''''<br /> Choose the suitable size for the patient, and then put the cap on his head in the correct position:<br /> - measure his head size (from the nasion NS to the ….) this is the 100% of the measure<br /> - now</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=User:FabioBeltramini&diff=2658 2008-04-23T16:43:27Z

<p>FabioBeltramini: </p> <hr /> <div>== &quot;Fabio&quot; ==<br /> <br /> I'm working on [[BCI based on Motor Imagery]];<br /> We would allow people with severe motor disabilities to use their brain signals to control a cursor with two-dimensional movement...<br /> <br /> ----<br /> <br /> {{User<br /> |firstname=Fabio<br /> |lastname=Beltramini<br /> |email=fbeltramini@excite.it<br /> |advisor=MatteoMatteucci<br /> |projectpage=<br /> |photo=fabiobeltramini.jpg|thumb|MyPicture|100 px|<br /> <br /> }}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=User:FabioBeltramini&diff=2648 2008-04-22T16:11:34Z

<p>FabioBeltramini: </p> <hr /> <div>== &quot;Fabio&quot; ==<br /> <br /> I'm working on BCI (Brain Computer Interface) based on Motor Imagery;<br /> We would allow people with severe motor disabilities to use their brain signals to control a cursor with two-dimensional movement...<br /> <br /> ----<br /> <br /> {{User<br /> |firstname=Fabio<br /> |lastname=Beltramini<br /> |email=fbeltramini@excite.it<br /> |advisor=MatteoMatteucci<br /> |projectpage=<br /> |photo=fabiobeltramini.jpg|thumb|MyPicture|100 px|<br /> <br /> }}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=User:FabioBeltramini&diff=2647 2008-04-22T16:01:05Z

<p>FabioBeltramini: /* &quot;&quot; */</p> <hr /> <div>== &quot;&quot; ==<br /> <br /> I'm working on BCI (Brain Computer Interface) based on Motor Imagery; our goal is control a two-dimensional movement signal <br /> <br /> <br /> <br /> <br /> <br /> <br /> ----<br /> <br /> {{User<br /> |firstname=Fabio<br /> |lastname=Beltramini<br /> |email=fbeltramini@excite.it<br /> |advisor=MatteoMatteucci<br /> |projectpage=<br /> |photo=fabiobeltramini.jpg|thumb|MyPicture|100 px|<br /> <br /> }}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=User:FabioBeltramini&diff=2646 2008-04-22T15:57:07Z

<p>FabioBeltramini: /* &quot;Hi!&quot; */</p> <hr /> <div>== &quot;&quot; ==<br /> <br /> <br /> <br /> <br /> <br /> ----<br /> <br /> {{User<br /> |firstname=Fabio<br /> |lastname=Beltramini<br /> |email=fbeltramini@excite.it<br /> |advisor=MatteoMatteucci<br /> |projectpage=<br /> |photo=fabiobeltramini.jpg|thumb|MyPicture|100 px|<br /> <br /> }}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=User:FabioBeltramini&diff=2645 2008-04-22T15:56:28Z

<p>FabioBeltramini: </p> <hr /> <div>== &quot;Hi!&quot; ==<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> {{User<br /> |firstname=Fabio<br /> |lastname=Beltramini<br /> |email=fbeltramini@excite.it<br /> |advisor=MatteoMatteucci<br /> |projectpage=<br /> |photo=fabiobeltramini.jpg|thumb|MyPicture|100 px|<br /> <br /> }}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=User:FabioBeltramini&diff=2644 2008-04-22T15:56:05Z

<p>FabioBeltramini: </p> <hr /> <div>== &quot;Hi!&quot; ==<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> {{User<br /> |firstname=Fabio<br /> |lastname=Beltramini<br /> |email=fbeltramini@excite.it<br /> |advisor=MatteoMatteucci<br /> |projectpage=<br /> |photo=fabiobeltramini.jpg|thumb|MyPicture|200 px|<br /> <br /> }}</div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=File:Fabiobeltramini.jpg&diff=2643 2008-04-22T15:55:43Z

<p>FabioBeltramini: </p> <hr /> <div></div>

FabioBeltramini https://airwiki.elet.polimi.it/index.php?title=User:FabioBeltramini&diff=2642 2008-04-22T15:55:22Z

<p>FabioBeltramini: </p> <hr /> <div>== &quot;Hi!&quot; ==<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> {{User<br /> |firstname=Fabio<br /> |lastname=Beltramini<br /> |email=fbeltramini@excite.it<br /> |advisor=MatteoMatteucci<br /> |projectpage=<br /> |photo=fabiobeltramini.jpg|thumb|MyPicture|300 px|<br /> <br /> }}</div>

FabioBeltramini