JBiSE> Vol.1 No.1, May 2008

Pattern Recognition of Motor Imagery EEG using Wavelet Transform

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ABSTRACT

Brain-computer interface (BCI) provides new communication and control channels that do not depend on the brain’s normal output of peripheral nerves and muscles. In this paper, we report on results of developing a single trial online motor imagery feature extraction method for BCI. The wavelet coefficients and autoregressive parameter model was used to extraction the features from the motor imagery EEG and the linear discriminant analysis based on mahalanobis distance was utilized to classify the pattern of left and right hand movement imagery. The performance was tested by the Graz dataset for BCI competition 2003 and satisfactory results are obtained with an error rate as low as 10.0%.

Cite this paper

Xu, B. and Song, A. (2008) Pattern Recognition of Motor Imagery EEG using Wavelet Transform. Journal of Biomedical Science and Engineering, 1, 64-67. doi: 10.4236/jbise.2008.11010.

References

[1] J. Virts, “The Third International Meeting on Brain-Computer Interface Technology: Making a Difference,” IEEE Trans .Neural. Syst. Rehabil. Eng., vol. 14, pp. 126-127, 2006.
[2] T. M. Vaughan, “Brain-computer Interface Technology: A Review of the Second International Meeting,” IEEE Trans .Neural. Syst. Rehabil. Eng., vol. 11, pp. 94-109, 2003.
[3] J. R. Wolpaw, N. Birbaumer, and W. Heetderks, et al,. “Brain-computer Interface Technology: A Review of the First International Meeting ,” IEEE Trans. Rehabil. Eng., vol. 8, pp. 164-173, 2000.
[4] J. R. Wolpaw, N. Birbaumer, and D. J. McFarland, et al, “Brain-computer interface for communication and control ,”Clinical Neurophysiology, vol. 113, pp. 767-791, 2002.
[5] B. Blankertz, K. R. Muller, and G. Curio, et al, “BCI Competition 2003—Progress and Perspectives in Detection and Discrimination of EEG Single Trials,” IEEE Trans. Rehabil. Eng., vol. 51, pp. 1044-1051, 2004.
[6] A. Schl鰃l, C. Neuper, and G. Pfurtscheller, 揈stimating the mutual information of an EEG-based Brain-Computer-Interface,?Biomedizinische. Technik., vol. 47, pp. 3-8, 2002.
[7] E. Houdayer, E. Labyt, and J. Cassim, at al, “Relationship between event-related beta synchronization and afferent inputs: analysis of finger movement and peripheral nerve stimulations ,” Clinical Neurophysiology, vol. 117, pp. 628-636, 2006.
[8] G. Pfurstcheller, F. H. Lopes da Silva, “ Event-related EEG/MEG synchronization and desynchronizaiton: basic principles,” Clinical Neurophysiology, vol. 110, pp. 1842-1857, 1999.
[9] G. Pfurstcheller, C. Neuper, “Motor imagery and Direct Brain-Computer Communication,” Proc. IEEE, vol. 89, pp. 1123-1134, 2001.
[10] A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert System with Application, in press.
[11] A. Subasi, “Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients,” Expert System with Application, vol. 28, pp. 701-711, 2005.
[12] A. Schlogl, “A new linear classification method for an EEG-based brain-computer interface,” unpublished.

  
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