nips nips2007 nips2007-106 nips2007-106-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike Hohlefeld, Klaus-Robert Müller, Vadim V. Nikulin
Abstract: Brain-Computer Interfaces can suffer from a large variance of the subject conditions within and across sessions. For example vigilance fluctuations in the individual, variable task involvement, workload etc. alter the characteristics of EEG signals and thus challenge a stable BCI operation. In the present work we aim to define features based on a variant of the common spatial patterns (CSP) algorithm that are constructed invariant with respect to such nonstationarities. We enforce invariance properties by adding terms to the denominator of a Rayleigh coefficient representation of CSP such as disturbance covariance matrices from fluctuations in visual processing. In this manner physiological prior knowledge can be used to shape the classification engine for BCI. As a proof of concept we present a BCI classifier that is robust to changes in the level of parietal α -activity. In other words, the EEG decoding still works when there are lapses in vigilance.
[1] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control”, Clin. Neurophysiol., 113: 767–791, 2002.
[2] N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor, “A spelling device for the paralysed”, Nature, 398: 297–298, 1999.
[3] G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, R. Ramoser, A. Schlögl, B. Obermaier, and M. Pregenzer, “Current Trends in Graz Brain-computer Interface (BCI)”, IEEE Trans. Rehab. Eng., 8(2): 216–219, 2000.
[4] J. Millán, Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, 2002.
[5] E. A. Curran and M. J. Stokes, “Learning to control brain activity: A review of the production and control of EEG components for driving brain-computer interface (BCI) systems”, Brain Cogn., 51: 326–336, 2003.
[6] G. Dornhege, J. del R. Millán, T. Hinterberger, D. McFarland, and K.-R. Müller, eds., Toward Brain-Computer Interfacing, MIT Press, Cambridge, MA, 2007.
[7] T. Elbert, B. Rockstroh, W. Lutzenberger, and N. Birbaumer, “Biofeedback of Slow Cortical Potentials. I”, Electroencephalogr. Clin. Neurophysiol., 48: 293–301, 1980.
[8] C. Guger, H. Ramoser, and G. Pfurtscheller, “Real-time EEG analysis with subject-specific spatial patterns for a Brain Computer Interface (BCI)”, IEEE Trans. Neural Sys. Rehab. Eng., 8(4): 447–456, 2000.
[9] B. Blankertz, G. Curio, and K.-R. Müller, “Classifying Single Trial EEG: Towards Brain Computer Interfacing”, in: T. G. Diettrich, S. Becker, and Z. Ghahramani, eds., Advances in Neural Inf. Proc. Systems (NIPS 01), vol. 14, 157–164, 2002.
[10] L. Parra, C. Alvino, A. C. Tang, B. A. Pearlmutter, N. Yeung, A. Osman, and P. Sajda, “Linear spatial integration for single trial detection in encephalography”, NeuroImage, 7(1): 223–230, 2002.
[11] E. Curran, P. Sykacek, S. Roberts, W. Penny, M. Stokes, I. Jonsrude, and A. Owen, “Cognitive tasks for driving a Brain Computer Interfacing System: a pilot study”, IEEE Trans. Rehab. Eng., 12(1): 48–54, 2004.
[12] J. Millán, F. Renkens, J. M. no, and W. Gerstner, “Non-invasive brain-actuated control of a mobile robot by human EEG”, IEEE Trans. Biomed. Eng., 51(6): 1026–1033, 2004.
[13] N. J. Hill, T. N. Lal, M. Schröder, T. Hinterberger, B. Wilhelm, F. Nijboer, U. Mochty, G. Widman, C. E. Elger, B. Schölkopf, A. Kübler, and N. Birbaumer, “Classifying EEG and ECoG Signals without Subject Training for Fast BCI Implementation: Comparison of NonParalysed and Completely Paralysed Subjects”, IEEE Trans. Neural Sys. Rehab. Eng., 14(6): 183–186, 2006.
[14] B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Müller, and G. Curio, “The non-invasive Berlin Brain-Computer Interface: Fast Acquisition of Effective Performance in Untrained Subjects”, NeuroImage, 37(2): 539–550, 2007, URL http://dx.doi.org/10.1016/j.neuroimage.2007.01.051.
[15] B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K.-R. Müller, “Optimizing Spatial Filters for Robust EEG Single-Trial Analysis”, IEEE Signal Proc. Magazine, 25(1): 41–56, 2008, URL http://dx.doi.org/10.1109/MSP.2008.4408441.
[16] S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A. Smola, and K.-R. Müller, “Invariant Feature Extraction and Classification in Kernel Spaces”, in: S. Solla, T. Leen, and K.-R. Müller, eds., Advances in Neural Information Processing Systems, vol. 12, 526–532, MIT Press, 2000.
[17] H. Berger, “Über das Elektroenkephalogramm des Menschen”, Archiv für Psychiatrie und Nervenkrankheiten, 99(6): 555–574, 1933.
[18] H. Jasper and H. Andrews, “Normal differentiation of occipital and precentral regions in man”, Arch. Neurol. Psychiat. (Chicago), 39: 96–115, 1938.
[19] G. Pfurtscheller and F. H. L. da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles”, Clin. Neurophysiol., 110(11): 1842–1857, 1999.
[20] Z. J. Koles, “The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG”, Electroencephalogr. Clin. Neurophysiol., 79(6): 440–447, 1991.
[21] K. Fukunaga, Introduction to statistical pattern recognition, Academic Press, Boston, 2nd edn., 1990.
[22] B. Schölkopf, Support vector learning, Oldenbourg Verlag, Munich, 1997.
[23] G. Dornhege, B. Blankertz, G. Curio, and K.-R. Müller, “Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms”, IEEE Trans. Biomed. Eng., 51(6): 993–1002, 2004.
[24] F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel, Robust Statistics: The Approach Based on Influence Functions, Wiley, New York, 1986.
[25] F. Critchley, “Influence in principal components analysis”, Biometrika, 72(3): 627–636, 1985.
[26] M. Romanazzi, “Influence in Canonical Correlation Analysis”, Psychometrika, 57(2): 237–259, 1992.
[27] B. Blankertz, K.-R. Müller, G. Curio, T. M. Vaughan, G. Schalk, J. R. Wolpaw, A. Schlögl, C. Neuper, G. Pfurtscheller, T. Hinterberger, M. Schröder, and N. Birbaumer, “The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials”, IEEE Trans. Biomed. Eng., 51(6): 1044–1051, 2004.
[28] B. Blankertz, K.-R. Müller, D. Krusienski, G. Schalk, J. R. Wolpaw, A. Schlögl, G. Pfurtscheller, J. del R. Millán, M. Schröder, and N. Birbaumer, “The BCI Competition III: Validating Alternative Approachs to Actual BCI Problems”, IEEE Trans. Neural Sys. Rehab. Eng., 14(2): 153–159, 2006. 8