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Brain-computer interfaces require online processing of electroencephalogram (EEG) measurements. Therefore, speed of signal processing is of great importance in BCI systems. We present a method of feature reduction by combining frequency band powers of EEG, in order to speed up processing and meanwhile avoid classifier overfitting. As a result a linear combination of power spectrum of EEG frequency bands (alpha, beta, gamma, delta & theta) was found that reduces the dimension of feature vector by a factor of 5. This method gives a total correct classification rate of 91.71% comparing to 87.96% achieved from direct use of frequency band powers and 85.54% achieved from PCA feature reduction method applied to the same feature vector with 14 components.
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PMID:Combination of frequency bands in EEG for feature reduction in mental task classification. 1794 25