Lunch Talk onBrain-Computer Interfacing Artificial Intelligence, University of Groningen
Pieter Laurens BaljonDecember 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 nerveendings 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 pipeline1. Preprocessing2. Feature extraction3. 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
• 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: 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/ExhibitBoothLetter grid is taken from the BCI2000 manual. It is an excerpt from a trial with a P300 speller application.
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