Optimizing Spatial filters for Robust EEG Single-Trial Analysis

Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a rather blurred image of brain activity. Therefore spatial filters are extremely useful in single-trial analysis in order to improve the signal-to-noise ratio. There are powerful methods from machine learning and signal processing that permit the optimization of spatio-temporal filters for each subject in a data dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular method in brain-computer interface (BCD research. Apart from reviewing several variants of the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance, briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing in our studies of the Berlin BCI (BBCI) project.

[1]  José del R. Millán,et al.  An Introduction to Brain-Computer Interfacing , 2007 .

[2]  José del R. Millán,et al.  Evaluation Criteria for BCI Research , 2007 .

[3]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[4]  B. Schölkopf,et al.  Logistic Regression for Single Trial EEG Classification , 2007 .

[5]  D J McFarland,et al.  Brain-computer interface research at the Wadsworth Center. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  G. Pfurtscheller,et al.  A fully automated correction method of EOG artifacts in EEG recordings , 2007, Clinical Neurophysiology.

[7]  Klaus-Robert Müller,et al.  Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.

[8]  Terrence J. Sejnowski,et al.  Toward Brain-Computer Interfacing (Neural Information Processing) , 2007 .

[9]  Rajesh P. N. Rao,et al.  Towards adaptive classification for BCI , 2006, Journal of neural engineering.

[10]  Klaus-Robert Müller,et al.  Toward noninvasive brain-computer interfaces , 2006, IEEE Signal Process. Mag..

[11]  D. Tucker,et al.  EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. , 1997, Electroencephalography and clinical neurophysiology.

[12]  José del R. Millán,et al.  General Signal Processing and Machine Learning Tools for BCI Analysis , 2007 .

[13]  Yuanqing Li,et al.  An Extended EM Algorithm for Joint Feature Extraction and Classification in Brain-Computer Interfaces , 2006, Neural Computation.

[14]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Klaus Linkenkaer-Hansen,et al.  Dynamics of mu-rhythm suppression caused by median nerve stimulation: a magnetoencephalographic study in human subjects , 2000, Neuroscience Letters.

[16]  Guido Dornhege,et al.  Increasing information transfer rates for brain-computer interfacing , 2006 .

[17]  Lucas C. Parra,et al.  Blind Source Separation via Generalized Eigenvalue Decomposition , 2003, J. Mach. Learn. Res..

[18]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[19]  K.-R. Muller,et al.  The Berlin brain-computer interface: EEG-based communication without subject training , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[21]  PROCEssIng magazInE IEEE Signal Processing Magazine , 2004 .

[22]  Motoaki Kawanabe,et al.  Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing , 2007, NIPS.

[23]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States , 2006, J. Univers. Comput. Sci..

[24]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[25]  Benjamin Blankertz,et al.  A Note on Brain Actuated Spelling with the Berlin Brain-Computer Interface , 2007, HCI.

[26]  R. Bracewell The Fourier Transform and Its Applications , 1966 .

[27]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[28]  Reinhold Scherer,et al.  A fully on-line adaptive BCI , 2006, IEEE Transactions on Biomedical Engineering.

[29]  H. Jasper,et al.  Electrocorticograms in man: Effect of voluntary movement upon the electrical activity of the precentral gyrus , 1949 .

[30]  Klaus-Robert Müller,et al.  Spatio-spectral filters for improving the classification of single trial EEG , 2005, IEEE Transactions on Biomedical Engineering.

[31]  H. Berger Über das Elektrenkephalogramm des Menschen , 1933, Archiv für Psychiatrie und Nervenkrankheiten.

[32]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[33]  Bernhard Schölkopf,et al.  Time-Dependent Demixing of Task-Relevant EEG Signals , 2006 .

[34]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[35]  G. Pfurtscheller,et al.  Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[36]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[37]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface , 2008, WCCI.

[38]  S. Scott Neuroscience: Converting thoughts into action , 2006, Nature.

[39]  H. Berger Über das Elektrenkephalogramm des Menschen , 1929, Archiv für Psychiatrie und Nervenkrankheiten.

[40]  B. Silverman,et al.  The Stationary Wavelet Transform and some Statistical Applications , 1995 .

[41]  Hans Berger,et al.  Über das Elektrenkephalogramm des Menschen , 1933, Archiv für Psychiatrie und Nervenkrankheiten.

[42]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[43]  Reinhold Scherer,et al.  Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

[44]  Kazuyuki Aihara,et al.  Classifying matrices with a spectral regularization , 2007, ICML '07.

[45]  Jdel.R. Millan,et al.  On the need for on-line learning in brain-computer interfaces , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[46]  Klaus-Robert Müller,et al.  Optimizing spatio-temporal filters for improving Brain-Computer Interfacing , 2005, NIPS.

[47]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[48]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[49]  Bernhard Schölkopf,et al.  Learning Optimal EEG Features Across Time, Frequency and Space , 2006, NIPS 2006.

[50]  Klaus-Robert Müller,et al.  Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach , 2006, NIPS.

[51]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Selina Wriessnegger,et al.  Regularised CSP for Sensor Selection in BCI , 2006 .

[53]  H. L. Andrews,et al.  ELECTRO-ENCEPHALOGRAPHY: III. NORMAL DIFFERENTIATION OF OCCIPITAL AND PRECENTRAL REGIONS IN MAN , 1938 .

[54]  K. Aihara,et al.  An Iterative Algorithm for Spatio-Temporal Filter Optimization , 2006 .

[55]  V. Jousmäki,et al.  Involvement of Primary Motor Cortex in Motor Imagery: A Neuromagnetic Study , 1997, NeuroImage.

[56]  Z J Koles,et al.  The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. , 1991, Electroencephalography and clinical neurophysiology.

[57]  Lucas C. Parra,et al.  Recipes for the linear analysis of EEG , 2005, NeuroImage.

[58]  G. Pfurtscheller,et al.  Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. , 1979, Electroencephalography and clinical neurophysiology.

[59]  R. Hari,et al.  Human cortical oscillations: a neuromagnetic view through the skull , 1997, Trends in Neurosciences.

[60]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.