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Pattern Recognition of Motor Imagery EEG using Wavelet Transform
School of Instrument Science and Engineering Southeast University,Nanjing 210096 China
School of Instrument Science and Engineering Southeast University,Nanjing 210096 China
- 1 School of Instrument Science and Engineering Southeast University,Nanjing 210096 China
- 2 School of Instrument Science and Engineering Southeast University,Nanjing 210096 China
Journal of Biomedical Science and Engineering·Volume 01 (2008)·Pages 64–67·Published 1 May 2008·DOI10.4236/jbise.2008.11010
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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%.
KeywordsBrain-computer interface (BCI)Motor imageryWavelet CoefficientsAutoregressive Model
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