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Research of feature extraction of BCI based on common spatial pattern and wavelet packet decomposition

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4 Author(s)
Ye Ning ; Inf. Sci. & Eng. Coll., Northeastern Univ., Shenyang, China ; Mei Zhan ; Sun Yuge ; Wang Xu

Brain-computer interface (BCI) is to establish a new communication system that translates human intentions reflected by EEG into a control signal for an output device such as a computer. This paper classified the EEG of two kinds of motor imagery. The feature extraction method combines wavelet packet decomposition and common spatial pattern. The k-nearest neighbors (KNN) is applied as classification method. The raw multi-channel EEG data is pre-processed by wavelet packet decomposition, with CSP method to extract the feature, and the best classification accuracy can reach 95.3%.If the EEG data is not decomposed by wavelet packet, the classification accuracy is only 83.3%. The result shows that if wavelet packet function and level is selected properly, the classification accuracy can improve effectively.

Published in:

Control and Decision Conference, 2009. CCDC '09. Chinese

Date of Conference:

17-19 June 2009