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- W2000632950 abstract "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." @default.
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- W2000632950 date "2009-06-01" @default.
- W2000632950 modified "2023-09-27" @default.
- W2000632950 title "Research of feature extraction of BCI based on common spatial pattern and wavelet packet decomposition" @default.
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- W2000632950 doi "https://doi.org/10.1109/ccdc.2009.5194997" @default.
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