A New Method of EEG Classification for BCI with Feature Extraction Based on Higher Order Statistics of Wavelet Components and Selection with Genetic Algorithms
A new method of feature extraction and selection of EEG signal for brain-computer interface design is presented. The proposed feature selection method is based on higher order statistics (HOS) calculated for the details of discrete wavelets transform (DWT) of EEG signal. Then a genetic algorithm is used for feature selection. During the experiment classification is conducted on a single trial of EEG signals. The proposed novel method of feature extraction using HOS and DWT gives more accurate results then the algorithm based on discrete Fourier transform (DFT).
Keywordsfeature extraction feature selection genetic algorithms (GA) higher order statistics (HOS) discrete wavelet transform (DWT) brain-computer interface (BCI) data-mining
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