Brain-Computer InterfaceOverview, methods and opportunitiesEmtiyaz (Emt)CS, UBC
Overview• BCI : application, structure, challenges, opportunities• Two examples of BCI : P300 and ERD/ERS• ERD/ERS BCI – Methods• ERD/ERS BCI – Our Work
Please don’t sleep, we will watch videoshttp://www.youtube.com/watch?v=NIG47YgndP8
http://www.youtube.com/watch?v=qCSSBEXBCbY
Why a Brain-Computer Interface?1.Use the capabilities of remaining pathways2.Detour around the points of damage (FES)3.Or if you have enough time to kill, use BCI
Military Applications Other Fancy Use
Locked-in SyndromeAmyotrophic Lateral SclerosisMultiple Sclerosis
Image Bayliss’ Thesis, and various webpages
Why a Brain-Computer Interface?
A General Brain-Computer Interface (BCI)
Image Pfurtscheller 2006
Evoked Potentials
Operant Conditions
Implanted Methods
BCI: Challenges and Opportunities
Image Pfurtscheller 2006
Evoked Potentials
Operant Conditions
Implanted Methods
ChallengesHigh Signal VariabilityUnderlying Physics unknownLinear or nonlinear Model?Man-Machine learning dilemma
One obvious opportunityOptimization of Electrode position
P300
ERD/ERS
P300 BCI
Images from http://ida.first.fraunhofer.de/projects/bci/competition_ii/albany_desc/albany_desc_ii.html
AverageBCI competetion II DatasetTask relevant stimuli
Non-task relevant response
Sequential data, Discrete o/p, Correlation based models
ERD/ERS BCIAmplitude attenuation/ enhancementin the specific frequency bands
Photographs from Pfurscheller 2002
Sequential data, Continuos o/p,
Strong Spectral charasteristics
C3 C4
ERD/ERS – Methods
Inter-trial Variance (IV) Method
Photographs from Pfurtscheller 1998
Only offline
Sensitive to frequency
band selection
RLS ApproachAdaptive Autoregressive (AAR) Model
Solved with Recursive least square (RLS) algorithms and features classified with Linear Discriminant Analysis (LDA)
observation noise
Photographs from Pfurtscheller 2000
Our WorkDone at the Indian Institute of Science, Bangalore, Indiain 2002-04
PublishedM. E. Khan and D. N. Dutt, "An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization(ERD) Estimation from EEG", Vol. 54, No. 7, July 2007, IEEE Transactions on Biomedical Engineering
Time-varying AR Model
RLS algorithm
Kalman filters
Use EM to get the parameters{A, Q, R, x0, S0)
EMKS
RLS
KS
Effect of EM Learning on Tracking
Average
Single TrialSignal
Optimization
Improved AR coefficientsTracking, and variance isreduced
Spectrum EstimationOptimization
Signal
Single Trial
Average
Improved Frequency TrackingVariance is reduced
Motor Imagery Data: Spectrum estimatesSignal
Single Trial Spectrum Estimate
Right Hand
Left Hand
Motor Imagery Data : ERD estimation
Final Comments• BCI datasets provide opportunity to test new
algorithms• Every two year there is a BCI competition. Some free
datasets can be found there (just google BCI competition)
• Do it for fun and blame it on humanity!!
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