Single-trial EEG classification for brain-computer interface using wavelet decomposition

13th European Signal Processing Conference, EUSIPCO 2005

Volumn , Issue , 2005, Pages 373-376

  Yong, Y P A ^a   Hurley, N J ^a   Silvestre, G C M ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN ACTIVITY; CLASSIFICATION ACCURACY; CLASSIFICATION SYSTEM; EEG SIGNALS; EFFECTIVE TOOL; EVENT RELATED DESYNCHRONIZATION; FEATURE VECTORS; KEY FEATURE; SINGLE-TRIAL ANALYSIS; SINGLE-TRIAL EEG; VOLUNTARY MOVEMENT;

WAVELET ANALYSIS;

WAVELET DECOMPOSITION;

EID: 84863678377     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. J. D. Bayliss and D. H. Ballard. Single trial p300 recognition in a virtual environment. In CIMA'99 (Soft Computing in Biomedicine), Rochester, NY, June 22-25 1999. CIMA'99.
2. 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, editors, Advances in Neural Information Processing Systems (NIPS 01), volume 14. MIT Press, 2002.
3. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
4. T. M. Cover. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions of Electronic Computing, EC-14:326-334, 1965.
5. I. Daubechies. Ten lectures on wavelets. In CMBS, pages 258-261. SIAM, 1994.
6. R. Der and U. Steinmetz. Wavelet analysis of eeg signals as a tool for the investigation of the time architecture of cognitive processes. Technical Report Report Nr.4/1997, des Instituts fuer Informatik der Universitaet Leipzig, 1997.
7. D. Garrett, D. A. Peterson, C. W. Anderson, and M. H. Thaut. Comparison of linear, nonlinear, and feature selection methods for eeg signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2):141-144, 2003.
8. T. Hinterberger, B. Wilheim, R. Veit, N. Weiskopf, T. N. Lal, and N. Birbaumer. Neural mechanisms underlyingcontrol of a brain-computer-interface (bci): Simultaneous recording of bold-response and eeg. (poster), 2004. (submitted).
9. S. Kelly, D. Burke, P. de Chazal, and R. Reilly. Parametric models and spectral analysis for classification in brain-computer interfaces. In Proceedings of 14th International Conference on Digital Signal Processing, Greece, July 2002.
10. R. Krepki, B. Blankertz, G. Curio, and K.-R. Müller. The berlin brain-computer interface (bbci): towards a new communication channel for online control of multimedia applications and computer games. In 9th International Conference on Distributed Multimedia Systems (DMS'03), pages 237 - 244, 2003.
11. E. Lalor, S. P. Kelly, C. Finucane, R. Burke, R. Smith, R. B. Reilly, and G. McDarby. Steady-state vep-based brain computer interface control in an immersive 3-d gaming environment. Journal of Applied Signal Processing, 2004. in press.
12. R. L. Ortolan, R. N. Mori, Jr. R. R. Pereira, C. M. N. Cabral, J. C. Pereira, and Jr. A. Cliquet. Evaluation of adaptive/nonadaptive filtering and wavelet transform techniques for noise reduction in emg mobile acquistion equipment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(1):60-69, March 2003.
13. R. Q. Quiroga. Obtaining single stimulua evoked potentials with wavelet denoising. Physica D, 145:278-252, 2000.
14. R. Q. Quiroga, O. W. Sakowitz, E. Basar, and M. Schürmann. Wavelet transform in the analysis of the frequency composition of evoked potentials. Brain Research Protocols, 8:16-24, 2001.
15. V. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, 2000.
16. J. R. Wolpaw, D. J. McFarland, G. W. Neat, and G. W. Forneris. An eeg-based brain-computer interface for cursor control. In Electroencepholog. Clin. Neurohysiol., volume 78, pages 252 - 259, March 1991.