Abstract
To solve the problem of optimal wavelet basis function selection in feature extraction of motor imagery electroencephalogram (MI-EEG) by wavelet packet transformation (WPT), based on the analysis of wavelet packet transformation and wavelet basis parameters, combine with the characteristics of MI-EEG, the characteristics of wavelet basis function suitable for feature extraction of MI-EEG are summarized. On the basis of processing and analyzing of two BCI competition data sets, signal to noise ratio (SNR), root mean squared error (RMSE), classification accuracy, and kappa value are introduced as evaluation criteria for feature extraction effect, it is concluded that the rbio2.2 wavelet basis function is the optimal wavelet basis function for feature extraction of MI-EEG. Finally, the MI-EEG collected in the laboratory is processed and analyzed, further proving that the rbio2.2 wavelet basis function is the optimal wavelet basis function for feature extraction of MI-EEG.
Highlights
Wavelet packet transformation with good time-frequency localization property has a good application in feature extraction of motor imagery electroencephalogram (MI-EEG) [1]
The optimal wavelet basis function of each cluster are compared horizontally, it can be seen that rbio2.2 wavelet basis function is optimal in signal to noise ratio (SNR), root mean squared error (RMSE), classification accuracy, and kappa value
The optimal wavelet basis function of each cluster are compared horizontally, it can be seen that sym3 wavelet basis function is optimal in SNR, RMSE, classification accuracy, and kappa value
Summary
Wavelet packet transformation with good time-frequency localization property has a good application in feature extraction of MI-EEG [1]. The comparison and selection of wavelet basis functions is always a difficult problem in the application of wavelet packet transformation. Servín-Aguilar et al applied Haar, Daubechies, and Coiflets to wavelet transform the EEG signals, compared of three different criteria: normalized mean square error (NMSE), percent root mean square percentage (PRD), and compression ratio (CR), Haar wavelet had better performance of EEG signal processing [8]. Yan et al in the wavelet packet transformation extraction of EEG signal characteristics, the best basis function algorithm was used to automatically select the most suitable wavelet basis function, wavelet methods used include Daubechies, Coiflets, and Symlets.
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