Difference between revisions of "BCI based on Motor Imagery"

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(→‎Part 3: Project tracking)
(→‎Part 3: Project tracking)
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** Achieving a method for pipe branching in BCI2000
 
** Achieving a method for pipe branching in BCI2000
 
** Beginning acquisition of data with ErrPs
 
** Beginning acquisition of data with ErrPs
 +
** Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required)
 +
Week 5:
 +
** Focusing on MIS to learn methods for smart use of error probability in class selection
  
 
== '''Part 4: References''' ==
 
== '''Part 4: References''' ==

Revision as of 12:54, 7 September 2009

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 analysis of brain waves measured on the human scalp.

Dates

Start date: 01/12/2008

End date:

Website(s)

http://airlab.elet.polimi.it/index.php/airlab/theses_lab_projects/brain_computer_interfaces_based_on_motor_imagery

People involved

Project head(s)
Other Politecnico di Milano people
Students currently working on the project

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 Safety norms will be followed.

Part 2: Project description

A Brain Computer Interface (BCI), also called Brain Machine Interface (BMI), is an advanced communication pathway that can allow an individual to control an external device, such as a wheelchair or a cursor on a computer, using signals measured from the brain (e.g., electroencephalography EEG). Research in this direction results of particular interest when addressed to totally paralyzed people. Using the mu and beta rhythms people has learnt to control their brain activity and thus to control external devices, such as a wheelchair, a cursor on a screen etc. We want to develop a system able to allow users to control the movement of an external device, controlling his/her mu or beta rhythms.

Part 3: Project tracking

Before 25/07/2009 some work as been done:

  • literature about motor imagery in BCI field has been read
  • initial sessions for signal analysis have been performed
  • a feature extractor and a classifier have been trained to interpret brain's motor imagination
  • some utilities to deal with large amount of data have been deployed
  • everything has been ported from Matlab to BCI2000
  • feedback sessions have proven the functionality of the previous work
  • a BCI speller has been tested with the output of the classifier

After 25/07/2009:

  • Week1:
    • Start studying material from course of Methodologies for Intelligent Systems
    • Start studying of ERP for applications in motor imagery
    • Read "Toward An Integrated P300 And ErrP-Based Brain-Computer Interface" Dal Seno B.
    • Analyzing ErrP caused by classification results in a MI task
    • Deploying a MI interface with visual synchronization signal
  • Week2:
    • Interface with discrete feedback presentation ready for use
    • Interface with early classification feedback ready for testing
    • Started testing of newly created interfaces
    • Analizing P300+Errp code for merging
  • Week3:
    • Brand new interface for better stimulation of potentials developed
    • Major improvement in file management for Matlab offline analysis
    • Set up of acquisition software for the new interface
    • Development of scripts to approximatively determine the end of training phase (discarded)
    • Merging and adapting code of P300+ErrP to motor imagery tasks
  • Week 4:
    • Achieving a method for pipe branching in BCI2000
    • Beginning acquisition of data with ErrPs
    • Ended deployment of execution pipe, from acquisition to extraction and collection of ErrPs (future test required)

Week 5:

    • Focusing on MIS to learn methods for smart use of error probability in class selection

Part 4: References

  • Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.
  • Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791
  • EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.


Links

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