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:
• Rhythms:
• P300:
Bereitschafts potential
Alpha, mu, beta, etc.
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
• P300 is ‘evoked potential’
– Less training
– Indicate attended target
Tetraplegic operating a speller application
Outline of a P300
speller application.
When target
row/column is
highlighted, it
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:
– Features:
– Classification:
ICA, CSP
Spectral power, Hjorth
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.
•
•
•
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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.