Model-based source separation for multi-class motor imagery

European Signal Processing Conference

Volumn , Issue , 2008, Pages

  Gouy Pailler, C ^a   Congedo, M ^a   Jutten, C ^a   Brunner, C ^b   Pfurtscheller, G ^b  

^a CNRS   (France)

^b GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN-COMPUTER INTERFACES (BCI); CLASSIFICATION RATES; COMMON SPATIAL PATTERNS; LINEAR COMBINATIONS; MOTOR IMAGERY; MULTI-CLASS; MULTI-CLASS PROBLEMS; NON-STATIONARY SOURCES; PRIORI KNOWLEDGE; SPATIAL FILTERINGS; STATISTICAL INDEPENDENCE;

INDEPENDENT COMPONENT ANALYSIS; INTERFACES (COMPUTER); SIGNAL PROCESSING;

BRAIN COMPUTER INTERFACE;

EID: 84863734415     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. J. Anemüller, T. J. Sejnowski, and S. Makeig. Complex independent component analysis of frequencydomain electroencephalographic data. Neural Netw, 16(9):1311-1323, Nov 2003.
2. N. Birbaumer and L. G. Cohen. Brain-computerinterfaces (BCI): Communication and restoration of movement in paralysis. J Physiol, 579(3):621-636, Jan 2007.
3. B. Blankertz, M. Kawanabe, R. Tomioka, F. Hohlefeld, V. Nikulin, and K.-R. Müller. Invariant common spatial patterns: Alleviating nonstationarities in braincomputer interfacing. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, MA, 2008.
4. C. Brunner, M. Naeem, R. Leeb, B. Graimann, and G. Pfurtscheller. Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis. Pattern Recognition Letters, 28:957-964, 2007.
5. M. Congedo, F. Lotte, and A. Lécuyer. Classification of movement intention by spatially filtered electromagnetic inverse solutions. Phys Med Biol, 51(8):1971-1989, Apr 2006.
6. G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K.-R. Müller. Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans Biomed Eng, 53(11):2274-2281, Nov 2006.
7. C. Jutten and J. Hérault. Blind separation of sources, part 1: an adaptive algorithm based on neuromimetic architecture. Signal Process 24(1):1-10, 1991.
8. S. Lemm, B. Blankertz, G. Curio, and K.-R. Müller. Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans Biomed Eng, 52(9):1541-1548, Sep 2005.
9. J. Müller-Gerking, G. Pfurtscheller, and H. Flyvbjerg. Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol, 110(5):787-798, May 1999.
10. M. Naeem, C. Brunner, R. Leeb, B. Graimann, and G. Pfurtscheller. Seperability of four-class motor imagery data using independent components analysis. J Neural Eng, 3(3):208-216, Sep 2006.
11. G. Pfurtscheller, C. Brunner, A. Schlögl, and F. H. L. da Silva. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage, 31(1):153-159, May 2006.
12. D.-T. Pham. Blind separation of instantaneous mixture of sources via the Gaussian mutual information criterion. Signal Processing, 81(4):855-870, 2001.
13. D.-T. Pham and J.-F. Cardoso. Blind separation of instantaneous mixtures of nonstationary sources. IEEE Transactions on Signal Processing, 49(9):1837-1848, Sept. 2001.
14. W. Wu, X. Gao, and S. Gao. One-Versus-the-Rest (OVR) algorithm: An extension of common spatial patterns(CSP) algorithm to multi-class case. Conf Proc IEEE Eng Med Biol Soc, 3:2387-2390, 2005.