Brain-Computer Interfacing

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					                Lunch Talk on
Brain-Computer Interfacing
Artificial Intelligence, University of Groningen




                                      Pieter Laurens Baljon
                                       December 14, 2006
                                          12:30-13:00
                    Overview

• What is a BCI?
• EEG-based BCI
  – Preprocess, extract features, classify
  – Functional correlates of features
• Our BCI Setup
  – Online, offline and simulation
• Clinical- or theoretical relevance     (or both?)
                What is a BCI

• Interface between the brain and computer
  – Normally: hands and arms, voice
  – Could be deficient through stroke or ALS
• A BCI:
  – “must not depend on the brain’s normal output
    pathways of peripheral nerves and muscles”1
• Prosthesis connected to nerve
  endings is not a BCI
                                          What is a BCI




Adapted from Carmena et al. 2003, in PLoS Biology 1(2)
      What is a BCI
       (Spelling example)




YouTube: http://www.youtube.com/watch?v=yhR076duc8M
      What is a BCI
          (Pong example)




YouTube: http://www.youtube.com/watch?v=qCSSBEXBCbY
What is a BCI

•   Brain signal can come from
    –   Invasive electrodes
    –   Non-invasive measurements
        •   EEG, fMRI, etc.


•   Underlying assumption
    –   Intentions have discernible
        counterpart in brain signal
                EEG-based BCI

• Sub fields of EEG-based BCI:
  – Signal processing on the EEG
  – Cognitive task for the subject (psychology)
  – Designing computer application (HMS)


• Typical pattern-recognition pipeline
  1. Preprocessing
  2. Feature extraction
  3. Classification (not considered here)
           The EEG: Preprocessing

• Preprocessing
  – Recombining electrodes can improve SNR

1. Spatial Filtering
  – Laplacian filters
     • Subtract surrounding electrodes
     • Vary distance to surrounding electrodes


2. Statistical recombination
  – Independent-Component Analysis
  – Common-Spatial Patterns
        The EEG: Feature Extraction

• Signal is recorded in 2 or more conditions
  – Features should correlate with condition.
  – They must be detectable in single trial


• Two principal approaches:
  – Brute force machine learning
     • Combine all imaginable features
  – Features with a functional correlate
     • Potential shifts:   Bereitschafts potential
     • Rhythms:            Alpha, mu, beta, etc.
     • P300:               Particular waveform
 The EEG: Sensorimotor Rhythm (SMR)

• Function of periodical brain activity
• The predominance of a function
  – Expressed by spectral power
• Many rhythms are „idling-rhythms‟.
  – Alpha rhythm over occipetal lobe (~10Hz)
  – Mu rhythm over motor cortex (~10 Hz)
The EEG: Sensorimotor Rhythm (SMR)




         University college, London & TU Graz
         VR application, controlling a wheelchair
          The EEG: (SCP) & P300

• Slow cortical potentials:
  – Low-pass filtered signal
  – E.g. Bereitschafts potential
• Ability to self regulate
  – Also used for neurofeedback
  – To treat ADHD                  Tetraplegic operating a speller application



                                                               Outline of a P300
• P300 is „evoked potential‟                                   speller application.
                                                               When target
  – Less training                                              row/column is
                                                               highlighted, it

  – Indicate attended target
                                                               evokes a P300.
                    Training

• Subject: biofeedback
  – learning to control physiological „parameters‟
  – E.g. Heartrate, EEG-components
• System: any Pattern Recognition method
  – BCI competition: Different sorts of data


• Complexity of classifier
  – Reduces „meaningfulnes‟ of transformation?
                       Training

• No „continuous mutual learning‟.
  – Mostly epoch based
  – Update the system in between sessions
  – Danger of oscillations in feedback loop.


• There is no between-subjects design yet
  – Due to large inter-subject variability (?)
  – Could elucidate
     • Effect of non-linear vs. linear feedback on EEG
          Our BCI Setup (online)

• General purpose framework: BCI2000
• Modular setup for
  – Amplifier driver
  – Signal processing
  – Application


• Open-source Borland C++
• Large community: over 100 labs
• Initial problems running BCI experiments
           Our BCI Setup (offline)

• Offline analysis in MatLab
  – Framework to test pattern recognition
• Setup similar to BCI2000
• Simple addition of new features, thus far:
  – Preprocessing:    ICA, CSP
  – Features:         Spectral power, Hjorth
  – Classification:   HMM, kNN, LDA, SVM
       Our BCI Setup (simulation)

• Addition to BCI2000.
• Signal source can model SMR changes
• Collaboration with developers of BCI2000

• Simulation in order to:
  – reverse engineer inner workings of BCI2000
  – pretest settings for adaptivity
     Clinical- & Theoretical relevance

• Most of the research is on healthy subjects
• Clinical research poses problems:
  – Proper operation requires extensive training
  – ALS Patients are only to learn control if they
    had it before the injury.
  – Small body of potential subjects


• Birbaumer reports a
  “significant increase in quality of life”
  They normally cannot communicate at all.
                                            References
•   [1] J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E.
    Donchin, L. A. Quatrano, C. J. Robinson and T. M. Vaughan, “Brain-computer interface technology:
    A review of the first international meeting,” IEEE Transactions on rehabilitation engineering, vol. 8,
    pp. 164–173, 2000.

•   Slide 1. Cover of the book Mathilda, about a telekinetic girl. Illustration: Quentin Blake
•   Slide 3. PL Baljon (author) operating a BCI. Private collection. Photo: Mark Span.
•   Slide 5, 6. Movies from youtube, filmed at CeBIT from Fraunhofer BCI, Berlin BCI.
•   Slide 7. “Hans-Peter Salzmann gelang es 1996 erst nach monatelangem Training mit dem Thought
    Translation Device, den Cursor zu steuern.” Source : University of Tübingen
•   Slide 12. “Controlling a wheelchair in a VR application” Source: University college, London & TU
    Graz.
•   Slide 13. Tetraplegic operating a speller device: Source: NIBIB,
    http://www.nibib.nih.gov/NewsEvents/Calendar/ExhibitBooth
    Letter grid is taken from the BCI2000 manual. It is an excerpt from a trial with a P300 speller application.