AIRWiki - User contributions [en]

Feature Selection and Extraction for a BCI based on motor imagery

2009-06-17T07:24:33Z

FrancescoAmenta: New page: == '''Part 1: Project profile''' == === Project name === Spatial Filter application on a 4-class motor imagery BCI-system === Project short description === The aim of this project is ...

== '''Part 1: Project profile''' ==

=== Project name ===

Spatial Filter application on a 4-class motor imagery BCI-system

=== Project short description ===

The aim of this project is to explore and analyze algorithms, pointed out from literature, for spatial filtering in a BCI system based on 4-class motor imagery.

=== Dates ===
Start date: 0?/0?/2008

End date:

=== People involved ===

===== Project head(s) =====

* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)

===== Other Politecnico di Milano people =====

* [[User:RossellaBlatt | Rossella Blatt]] (phd student)

===== Students currently working on the project =====

* [[User:FrancescoAmenta | Francesco Amenta]] (master student)

=== Laboratory work and risk analysis ===

Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.
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.

== '''Part 2: Project description''' ==
Main goal of this project it's to analyse application of Spatial Filter algorithm on a BCI system based on Motor Imagery. Mainly we want to test how much spatial filter' application could improve classification accuracy and also how to select best filter strategy for specific situation/subject.
Some of the algorithms chosen for testing was developed to operate over bivariate data. To extend these, researchers have suggested a one-versus-rest approach. As subgoal inside main project's object we try to find a new way to extend algorithm to be able to operate over N-class contest.
We have developed an analysis environment to be able to implements algorithms, derive spatial filter matrix from train dataset and then evaluate result on testing dataset.
Selection of algorithm to analyze was made looking for the most performant once inside literature and also BCI Competition' results. After this preliminary selection three filter's algorithms was retained:
*ICA
*CSP
*CSSD

=== Brief description of Motor Imagery BCI architecture ===

Even if all BCI system share the same goal, create a new communication pathway from mind to an external device, this isn't always true for architecture, due to different type of brain signal observed. However, we could trace a common design shared from most BCI application. Project work was focused on Signal Processing block, more specifically on Signal Filtering and Features Extraction subblock.

=== Analysis Tool ===

An off-line analysis tool was developed to generate spatial filter starting from data recorded by BCI2000 environments. Tool also provide functionality to test and visualize filters' application result. Tool was develop inside Matlasb environment due to compatibility with BCI2000 analysis tool and also for best deal computation effort mainly based on matrix manipulations.

==== Prefiltering Module ====
Prefiltering module preprocess data to remove unuseful information for filter generation process. Application provides two kinds of proprecess step:

*''' Frequency filtering ''' [none,fir,iir] Bandpass filter imported data in selected frequency range. Provide Infinite Response Filter and Finite Response Filter.

==== Filter Selection Module ====

===== ICA =====

Independent Component Analysis is a blind source separation technique applied to recover source from a mixture signal observation.
ICA algorithm employees statistical mutual independence of component to extract source signal from mixture one. We found different algorithm depending on contrast function used to maximize independence. We analize two ICA algorithm, Infomax and FastICA, which seems to have good results when applied to BCI data.

Information maximization algorithm, also know as '''Infomax''', was based on approach developed from Bell & Sejnowski, that find a solution maximization the joint entropy of the output H(y). This is the same to minimize mutual information I(y) of the output of a neural network. An implementation of this algorithm was found...
Algorithm have many parameters:
* '''learning rate''' Learning rate of neural network learning algorithm. Default value was set to
* '''momentum''' Momentum of neural network learning algorithm. Default value was set to
* '''bias'''
* '''max step''' Maximum number of step that algorithm should be perform
* '''initial weight''' A random matrix of initial weight
* '''epsilon'''

'''FastICA''' algorithm search for independent component maximizing the negentropy J(y) of each mixtured signal.
Algorithm parameters are:
* '''approach''' Value could be ''symmetric'', in which all signal was ,or ''deflaction''
* '''nonlinarity''' Type of nonlinearity used to compute contrast function
* '''epsilon''' Lowest bound of prediction error
* '''max iteration'''
* '''sample size''' [0,1] Percentage value of imported data to use as training set

===== CSP =====

Common Spatial Pattern algorithm simultaneous diagonalize covariance matrix of class labelled signals to find a linear subspace, i.e., a linear transformation matrix, in such a way that combined features maximize variance for one class and minimize its for other class. Standard CSP is designed for binary classification's problem, then extension to multi-class problem was done, as suggest in literature, by splitting original problem into several binary decision, computing each filter like one-vs-rest. After computing n matrix, with n number of class to analize, then algorithm output a spatial matrix
Algorithm params that could be tuned are:
* '''sample size''' [0,1] Percentage value of imported data to use as training set
* '''retained eigenvalues''' Integer. Number(s) of eigenvalue(s) from each class matrix to retain in output spatial matrix.
* '''tao''' [0,6] Time window over trial. Define a time window over trial' data to use as training data.
* '''mode''' [1,2,3,4] Type of computed filter.
===== CSSD =====

Common Spatial Subsample Decomposition is a statistical method derived from common spatial pattern analysis. Also this method, like CSP, look for linear transformation of original signal to obtain combined features that hold maximum variance, i.e. most useful information for discriminant process, for selected class.
Algorithm params that could be tuned are:
* '''sample size''' [0,1] Percentage value of imported dato to use as training set
* '''retained eigenvalues''' Integer. Number(s) of eigenvalue(s) from each class matrix to retain in output spatial matrix.
* '''tao''' [0,6] Time window over trial. Define a time window over trial' data to use as training data.
* '''mode''' [1,2,3,4] Type of computed filter.
==== Filter Testing Module ====

This module provides testing features for generated filters. Module grant tuning of testing environments by which tests filters and selection of statistical analysis to perform. Module also provide a graphical representation of computed statistical measures.

Testing environment parameter are:
*'''Feature Channel'''
*'''Frequency Filter'''
*'''Classification algorithm'''
Statistical analysis type are:
*'''R-square'''
*'''Fisher ratio'''

=== Usage ===

User could access the tools by 3 main script. First script grant access to spatial filter computation. User must select subject, date of register session and witch type of filter would to compute. Second script use computed filter to preprocess data and then make a selection of extracted features trough genetic algorithm instructed to find a subset that maximize classification accuracy over train dataset. Then third and last script is designed to test filter and selected feature over test dataset.
== '''Part 3: References''' ==
* Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.
* Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791
* Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis, Brunner C., Naeem M., Leeb R., Graimann B., and Pfurtscheller G. 2007. Pattern Recogn. Lett. 28, 8 (Jun. 2007), 957-964.
* Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms, Dornhege G.; Blankertz B.; Curio G.; Muller K.-R., Biomedical Engineering, IEEE Transactions on , vol.51, no.6, pp.993-1002, June 2004
* Common spatial subspace decomposition applied to analysis of brain responses under multiple task conditions: a simulation study, Y. Wang, P. Berg, M. Scherg, Clinical Neurophysiology, Vol. 110, No. 4. (1 April 1999), pp. 604-614.
== '''Part 4: Project Tracking''' ==

Talk:Feature Selection and Extraction for a BCI based on motor imagery

2009-02-19T10:59:42Z

FrancescoAmenta:

== '''Part 1: Project profile''' ==

=== Project name ===

Feature Selection and Extraction for a BCI based on motor imagery

=== Project short description ===

The aim of this project is to explore and analyze methods, pointed out from literature, for feature selection and extraction in a BCI system based on motor imagery.

=== Dates ===
Start date: 0?/0?/2008

End date:

=== People involved ===

===== Project head(s) =====

* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)

===== Other Politecnico di Milano people =====

* [[User:RossellaBlatt | Rossella Blatt]] (phd student)

===== Students currently working on the project =====

* [[User:FrancescoAmenta | Francesco Amenta]] (master student)

=== Laboratory work and risk analysis ===

Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.
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.

== '''Part 2: Project description''' ==

L'obbiettivo principale che questo progetto si prefigge è l'analisi di algoritmi di filtraggio spaziale applicati in un sistema BCI basato su Motor Imagery. L'obbiettivo specifico dell'analisi è la valutazione dell'impatto dei suddetti algoritmi sulle capità di classificazione del sistema attualmente in fase di sviluppo presso IIT-Lab.
Selection of algorithm to analize was made looking for the most performant once inside literature and also BCI Competition' results. After this preliminary selection three filter's algorithm was retained:
*ICA
*CSP
*CSSD

=== Brief description of Motor Imagery BCI architecture ===

Even if all BCI system share the same goal, create a new communication pathway from mind to an external device, this isn't always true for architecture, due to different type of brian signal observed. However, we could trace a common design shared from most BCI application. Project work was focused on Signal Processing block, more specifically on Featur Filtering subblock.

=== Analysis Tool ===

An offline analysis tool was developed to generate spazial filter starting from data recorded by BCI2000 enviroments. Tool also provide functionality to test and visualize filters' application result. Tool was develop inside Matlasb eviroments due to compatibility with BCI2000 analysis tool and also for

==== Data Import Module ====

==== Prefiltering Module ====
Prefiltering module preprocess data to remove unuseful information for filter generation process. Application provides two kinds of proprecess step:
*''' Whitening ''' White imported data removing mean and normalizing variance to 1
*''' Frequency filtering ''' Bandpass filter imported data in selected frequency range

==== Filter Selection Module ====

===== ICA =====

Independent Component Analysis is a blind source separation tecnique applied to recover sorce from a mixture signal observation.
ICA algorithm emploies statistical mutal indipendence of component to extract soruce signal from mixture one. We found different algorithm depending on contrast funcion used to maximize independence. We analize two ICA algorithm, Infomax and FastICA, which seems to have good reults when applied to BCI data.

Infomation maximization algorithm, also kwon as '''Infomax''', was based on approach developed from Bell & Sejnowski, that find a solution maximization the joint entropy of the output H(y). This is the same to minimize mutual information I(y) of the output of a neural network. An implementation fo this algorithm was found...
Algorithm have many parameters:
* '''learning rate''' Learning rate of neural network learning algorithm. Default value was set to
* '''momentum''' Momentum of neural network learning algorithm. Default value was set to
* '''bias'''
* '''max step''' Maximum number of step that algorithm shuold be perform
* '''initial weight''' A random matrix of inizial weight
* '''epsilon'''

'''FastICA''' algorithm search for independent component maximizing the negentropy J(y) of each mixtured signal.
Algorithm parameters are:
* '''approach''' Value could be ''symmetric'', in wich all signal was ,or ''deflaction''
* '''nonlinarity''' Type of nonlinearity used to compute contrast function
* '''epsilon''' Lowest bound of prediction error
* '''max iteration'''
* '''sample size''' [0,1] Percentage value of imported dato to use as training set

===== CSP =====

Common Spatial Pattern algorithm simultaneus diagonalize covariance matrix of class labeled signals to find a linear subspace, i.e., a linear trasformation matrix, in such a way that combined features maximize variance for one class and minize its for other class. Standard CSP is designed for binary classification's problem, then extension to multi-class problem was done, as suggest in literature, by splitting original problem into several binary decision, computing each filter like one-vs-rest. After computing n matrix, with n number of class to analize, then algorithm output a spatila matrix
Algorithm params that could be tuned are:
* '''sample size''' [0,1] Percentage value of imported dato to use as training set
* '''retained eigenvalues''' Integer. Number(s) of eigenvalue(s) from each class matrix to retain in output spatial matrix.

===== CSSD =====

Common Spatial Subsample Decomposition is a statistical method derived from common spatial pattern analysis. Also this method, like CSP, look for linear trasformation of orginal signal to obtain combined features that hold maximum variance, i.e. most usefull information for discrimint process, for selected class.
Algorithm params that could be tuned are:
* '''sample size''' [0,1] Percentage value of imported dato to use as training set
* '''retained eigenvalues''' Integer. Number(s) of eigenvalue(s) from each class matrix to retain in output spatial matrix.


== '''Part 3: References''' ==
*

== '''Part 4: Project Tracking''' ==

Talk:Feature Selection and Extraction for a BCI based on motor imagery

2009-02-08T11:51:47Z

FrancescoAmenta:

== '''Part 1: Project profile''' ==

=== Project name ===

Feature Selection and Extraction for a BCI based on motor imagery

=== Project short description ===

The aim of this project is to explore and analyze methods, pointed out from literature, for feature selection and extraction in a BCI system based on motor imagery.

=== Dates ===
Start date: 0?/0?/2008

End date:

=== People involved ===

===== Project head(s) =====

* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)

===== Other Politecnico di Milano people =====

* [[User:RossellaBlatt | Rossella Blatt]] (phd student)

===== Students currently working on the project =====

* [[User:FrancescoAmenta | Francesco Amenta]] (master student)

=== Laboratory work and risk analysis ===

Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.
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.

== '''Part 2: Project description''' ==

L'obbiettivo principale che questo progetto si prefigge è l'analisi di algoritmi di filtraggio spaziale applicati in un sistema BCI basato su Motor Imagery. L'obbiettivo specifico dell'analisi è la valutazione dell'impatto dei suddetti algoritmi sulle capità di classificazione del sistema attualmente in fase di sviluppo presso IIT-Lab.
Selection of algorithm to analize was made looking for the most performant once inside literature and also BCI Competition' results. After this preliminary selection three filter's algorithm was retained:
*ICA
*CSP
*CSSD

=== Brief description of Motor Imagery BCI architecture ===

Even if all BCI system share the same goal, create a new communication pathway from mind to an external device, this isn't always true for architecture, due to different type of brian signal observed. However, we could trace a common design shared from most BCI application. Project work was focused on Signal Processing block, more specifically on Featur Filtering subblock.

=== Analysis Tool ===

An offline analysis tool was developed to generate spazial filter starting from data recorded by BCI2000 enviroments. Tool also provide functionality to test and visualize filters' application result. Tool was develop inside Matlasb eviroments due to compatibility with BCI2000 analysis tool and also for

==== Data Import Module ====

==== Prefiltering Module ====
Prefiltering module preprocess data to remove unuseful information for filter generation process. Application provides two kinds of proprecess step:
*''' Whitening ''' White imported data removing mean and normalizing variance to 1
*''' Frequency filtering ''' Bandpass filter imported data in selected frequency range

==== Filter Selection Module ====

===== ICA =====

Independent Component Analysis is a blind source separation tecnique applied to recover sorce from a mixture signal observation.
ICA algorithm emploies statistical mutal indipendence of component to extract soruce signal from mixture one. We found different algorithm depending on contrast funcion used to maximize independence. We analize two ICA algorithm, Infomax and FastICA, which seems to have good reults when applied to BCI data.

Infomation maximization algorithm, also kwon as '''Infomax''', was based on approach developed from Bell & Sejnowski, that find a solution maximization the joint entropy of the output H(y). This is the same to minimize mutual information I(y) of the output of a neural network. An implementation fo this algorithm was found...
Algorithm have many parameters:
* '''learning rate''' Learning rate of neural network learning algorithm. Default value was set to
* '''momentum''' Momentum of neural network learning algorithm. Default value was set to
* '''bias'''
* '''max step''' Maximum number of step that algorithm shuold be perform
* '''initial weight''' A random matrix of inizial weight
* '''epsilon'''

'''FastICA''' algorithm search for independent component maximizing the negentropy J(y) of each mixtured signal.
Algorithm parameters are:
* '''approach''' Value could be ''symmetric'', in wich all signal was ,or ''deflaction''
* '''nonlinarity''' Type of nonlinearity used to compute contrast function
* '''epsilon''' Lowest bound of prediction error
* '''max iteration'''
* '''sample size''' [0,1] Percentage value of imported dato to use as training set

===== CSP =====

Common Spatial Pattern algorithm simultaneus diagonalize covariance matrix of class labeled signals to find a linear subspace, i.e., a linear trasformation matrix, in such a way that combined features maximize variance for one class and minize its for other class. Standard CSP is designed for binary classification's problem, then extension to multi-class problem was done, as suggest in literature, by splitting original problem into several binary decision, computing each filter like one-vs-rest. Algorithm params that could be tuned are:
*

===== CSSD =====

Common Spatial Subsample Decomposition is a statistical method derived from common spatial pattern analysis. Also this method, like CSP, look for linear trasformation of orginal signal to obtain combined features that hold maximum variance, i.e. most usefull information for discrimint process, for selected class. Algorithm params that could be tuned are:
*


== '''Part 3: References''' ==
*

== '''Part 4: Project Tracking''' ==

Talk:Feature Selection and Extraction for a BCI based on motor imagery

2009-02-02T14:04:23Z

FrancescoAmenta:

== '''Part 1: Project profile''' ==

=== Project name ===

Feature Selection and Extraction for a BCI based on motor imagery

=== Project short description ===

The aim of this project is to explore and analyze methods, pointed out from literature, for feature selection and extraction in a BCI system based on motor imagery.

=== Dates ===
Start date: 0?/0?/2008

End date:

=== People involved ===

===== Project head(s) =====

* [[User:MatteoMatteucci | Matteo Matteucci]] (professor)

===== Other Politecnico di Milano people =====

* [[User:RossellaBlatt | Rossella Blatt]] (phd student)

===== Students currently working on the project =====

* [[User:FrancescoAmenta | Francesco Amenta]] (master student)

=== Laboratory work and risk analysis ===

Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line.
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.

== '''Part 2: Project description''' ==

L'obbiettivo principale che questo progetto si prefigge è l'analisi di algoritmi di filtraggio spaziale applicati in un sistema BCI basato su Motor Imagery. L'obbiettivo specifico dell'analisi è la valutazione dell'impatto dei suddetti algoritmi sulle capità di classificazione del sistema attualmente in fase di sviluppo presso IIT-Lab.
Selection of algorithm to analize was made looking for the most performant once inside literature and also BCI Competition' results. After this preliminary selection three filter's algorithm was retained:
*ICA
*CSP
*CSSD

=== Brief description of Motor Imagery BCI architecture ===

Even if all BCI system share the same goal, create a new communication pathway from mind to an external device, this isn't always true for architecture, due to different type of brian signal observed. However, we could trace a common design shared from most BCI application. Project work was focused on Signal Processing block, more specifically on Featur Filtering subblock.

=== Analysis Tool ===

An offline analysis tool was developed to generate spazial filter starting from data recorded by BCI2000 enviroments. Tool also provide functionality to test and visualize filters' application result. Tool was develop inside Matlasb eviroments due to compatibility with BCI2000 analysis tool and also for

==== Data Import Module ====

==== Prefiltering Module ====
Prefiltering module preprocess data to remove unuseful information for filter generation process. Application provides two kinds of proprecess step:
*''' Whitening ''' White imported data removing mean and normalizing variance to 1
*''' Frequency filtering ''' Bandpass filter imported data in selected frequency range

==== Filter Selection Module ====

===== ICA =====

Independent Component Analysis is a blind source separation tecnique applied to recover sorce from a mixture signal observation.
ICA algorithm emploies statistical mutal indipendence of component to extract soruce signal from mixture one. We found different algorithm depending on contrast funcion used to maximize independence. We analize two ICA algorithm, Infomax and FastICA, which seems to have good reults when applied to BCI data.

Infomation maximization algorithm, also kwon as '''Infomax''', was based on approach developed from Bell & Sejnowski, that find a solution maximization the joint entropy of the output H(y). This is the same to minimize mutual information I(y) of the output of a neural network. An implementation fo this algorithm was found...
Algorithm have many parameters:
* '''learning rate''' Learning rate of neural network learning algorithm. Default value was set to
* '''momentum''' Momentum of neural network learning algorithm. Default value was set to
* '''bias'''
* '''max step''' Maximum number of step that algorithm shuold be perform
* '''initial weight''' A random matrix of inizial weight
* '''epsilon'''

'''FastICA''' algorithm search for independent component maximizing the negentropy J(y) of each mixtured signal.
Algorithm parameters are:
* '''approach''' Value could be ''symmetric'', in wich all signal was ,or ''deflaction''
* '''nonlinarity''' Type of nonlinearity used to compute contrast function
* '''epsilon''' Lowest bound of prediction error
* '''max iteration'''
* '''sample size''' [0,1] Percentage value of imported dato to use as training set

===== CSP =====

Common Spatial Pattern algorithm simultaneus diagonalize covariance matrix of class labeled signals to find a linear subspace, i.e., a linear trasformation matrix, in such a way that combined features maximize variance for one class and minize its for other class. Standard CSP is designed for binary classification's problem, then extension to multi-class problem was done, as suggest in literature, by splitting original problem into several binary decision, computing each filter like one-vs-rest. Algorithm params that could be tuned are:
*

===== CSSD =====

Common Spatial Subsample Decomposition is a statistical method derived from common spatial pattern analysis. Also this method, like CSP, look for linear trasformation of orginal signal to obtain combined features that hold maximum variance, i.e. most usefull information for discrimint process, for selected class. Algorithm params that could be tuned are:
*

==== Filter Testing Module ====

This module provides testing features for generated filters. Module grant tunig of testing enviroments by which tests filters and selection of statistical analysis to perform. Module also provide a grafical rappresentation of computed statistical measures.

Testing eviroment parameter are:
*'''Feature Channel'''
*'''Frequncy Filter'''
*'''Classification algorithm'''
Statistical analysis type are:
*'''R-square'''
*'''Fisher ratio'''
== '''Part 3: References''' ==
*

== '''Part 4: Project Tracking''' ==

Talk:Feature Selection and Extraction for a BCI based on motor imagery

2009-01-31T17:15:11Z

FrancescoAmenta:

IIT-Lab

2009-01-21T10:29:09Z

FrancescoAmenta:

== What is the IIT-Lab ==

AIRLab-IITLab is dedicated to activities founded by the Italian Institute of Technology.
The lab hosts activities related to Brain-Computer Interfaces (BCI) and Affective Computing.

=== Location ===
It is located in the Rimembranze di Lambrate building of the Department of Electronics and Information, Via Rimembranze di Lambrate, 14, Milan.

=== Access Rules ===
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.

=== Booking ===

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.


{| border="1"
! Day !! Time !! Person !! Instrument
|-
|22 Jan || 15:00-18:00 || [[User:FabioBeltramini]] || EEG
|-
|}

== Links ==

* [[Brain-Computer Interface]] page on this Wiki
* [[Affective Computing]] page on this Wiki
* [http://www.airlab.elet.polimi.it/index.php/airlab/visitor_info/airlab_iitlab AIRLab - IITLab]

Electroencephalographs

2008-09-23T07:26:43Z

FrancescoAmenta:

== BeLight ==

A brief description of EbNeuro BeLight should go here.

=== Connectors ===
* '''1-21:''' EEG inputs
* '''A, B, C, D''' Bipolar inputs
* '''NE:''' EEG reference
* '''ISOGN:''' EEG ground (Isolated ground)
* '''22-24:''' Poly channels
* '''NEP:''' Poly reference

=== Booking ===



{| border="1"
! Day !! Time !! Person
|-
| Wednesday 24 September || 09:00-13:00 || Rossellla
|-
| Thursday 25 September || 10:00-19:00 || Bernardo
|}

=== Electrodes ===

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).

==== Maintenance ====

The paste or gel used during acquisitions must be thoroughly removed after use. Please follow the following procedure:
# Rinse the electrodes under running water. This step can remove only a part of the gel/paste. Please avoid washing or wetting the plugs.
# 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.
# Rinse the electrodes under running water (and also rinse the container).
# 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.
# 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.

Please take into account also the time for electrode cleaning when you plan your EEG acquisitions.

== Presets ==

Description of the presets saved in the Galileo software.

===P300===
Used for P300 and ErrP recordings. There are 4 EEG channels, 1 EOG, 2 external signals in DC; sampling frequency is 512 Hz.
;Channels and connectors:
:'''EOG''': EOG, input "A"; the "+" input for the electrode above the eye, the "-" input for the one below
:'''Fz''': Fz, input 7
:'''Cz''': Cz, input 12
:'''Pz''': Pz, input 17
:'''Oz''': Oz, input 20 (labeled as "O1" on the amplifier)
:'''Sync''': phototransistor for stimulus synchronization; positive lead (red) in input 22, negative lead (black) in input NEP
:'''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.

== Links ==

* [[Brain-Computer Interface]] page on this Wiki
* [[EEG Montage]] for instructions on how to mount electrodes

Electroencephalographs

2008-09-23T07:26:12Z

FrancescoAmenta:

== BeLight ==

A brief description of EbNeuro BeLight should go here.

=== Connectors ===
* '''1-21:''' EEG inputs
* '''A, B, C, D''' Bipolar inputs
* '''NE:''' EEG reference
* '''ISOGN:''' EEG ground (Isolated ground)
* '''22-24:''' Poly channels
* '''NEP:''' Poly reference

=== Booking ===



{| border="1"
! Day !! Time !! Person
|-
| Thursday 25 September || 10:00-19:00 || Bernardo
|-
| Wednesday 24 September || 09:00-13:00 || Rossellla
|}

=== Electrodes ===

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).

==== Maintenance ====

The paste or gel used during acquisitions must be thoroughly removed after use. Please follow the following procedure:
# Rinse the electrodes under running water. This step can remove only a part of the gel/paste. Please avoid washing or wetting the plugs.
# 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.
# Rinse the electrodes under running water (and also rinse the container).
# 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.
# 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.

Please take into account also the time for electrode cleaning when you plan your EEG acquisitions.

== Presets ==

Description of the presets saved in the Galileo software.

===P300===
Used for P300 and ErrP recordings. There are 4 EEG channels, 1 EOG, 2 external signals in DC; sampling frequency is 512 Hz.
;Channels and connectors:
:'''EOG''': EOG, input "A"; the "+" input for the electrode above the eye, the "-" input for the one below
:'''Fz''': Fz, input 7
:'''Cz''': Cz, input 12
:'''Pz''': Pz, input 17
:'''Oz''': Oz, input 20 (labeled as "O1" on the amplifier)
:'''Sync''': phototransistor for stimulus synchronization; positive lead (red) in input 22, negative lead (black) in input NEP
:'''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.

== Links ==

* [[Brain-Computer Interface]] page on this Wiki
* [[EEG Montage]] for instructions on how to mount electrodes

Electroencephalographs

2008-09-17T17:45:41Z

FrancescoAmenta:

== BeLight ==

A brief description of EbNeuro BeLight should go here.

=== Connectors ===
* '''1-21:''' EEG inputs
* '''A, B, C, D''' Bipolar inputs
* '''NE:''' EEG reference
* '''ISOGN:''' EEG ground (Isolated ground)
* '''22-24:''' Poly channels
* '''NEP:''' Poly reference

=== Booking ===



{| border="1"
! Day !! Time !! Person
|-
| Friday 19 September || 10:00-14:00 || Rossella
|-
| Monday 22 September || 10:00-19:00 || Bernardo
|}

=== Electrodes ===

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).

==== Maintenance ====

The paste or gel used during acquisitions must be thoroughly removed after use. Please follow the following procedure:
# Rinse the electrodes under running water. This step can remove only a part of the gel/paste. Please avoid washing or wetting the plugs.
# 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.
# Rinse the electrodes under running water (and also rinse the container).
# 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.
# 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.

Please take into account also the time for electrode cleaning when you plan your EEG acquisitions.

== Presets ==

Description of the presets saved in the Galileo software.

===P300===
Used for P300 and ErrP recordings. There are 4 EEG channels, 1 EOG, 2 external signals in DC; sampling frequency is 512 Hz.
;Channels and connectors:
:'''EOG''': EOG, input "A"; the "+" input for the electrode above the eye, the "-" input for the one below
:'''Fz''': Fz, input 7
:'''Cz''': Cz, input 12
:'''Pz''': Pz, input 17
:'''Oz''': Oz, input 20 (labeled as "O1" on the amplifier)
:'''Sync''': phototransistor for stimulus synchronization; positive lead (red) in input 22, negative lead (black) in input NEP
:'''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.

== Links ==

* [[Brain-Computer Interface]] page on this Wiki
* [[EEG Montage]] for instructions on how to mount electrodes

Talk:Feature Selection and Extraction for a BCI based on motor imagery

2008-06-29T08:28:09Z

FrancescoAmenta:

User:FrancescoAmenta

2008-06-09T15:08:13Z

FrancescoAmenta:

{{User
|firstname=Francesco
|lastname=Amenta
|email=francesco.amenta@gmail.com
|advisor=MatteoMatteucci
|projectpage= Feature Selection and Extraction for a BCI based on motor imagery
|photo=francescoamenta.png|thumb|MyPicture|100 px|

}}

File:Francescoamenta.png

2008-06-09T15:07:42Z

FrancescoAmenta: