Abstract
To solve the problem of optimal wavelet basis selection in motor imagery electroencephalogram (MI-EEG) denoising by wavelet transform, based on the analysis of wavelet basis parameters and characteristics, combined with the characteristics of MI-EEG, we summarized the characteristics of wavelet basis suitable for MI-EEG denoising. Signal to noise ratio (SNR) and root mean squared error (RMSE) are introduced as evaluation criteria of signal denoising effect, it is concluded that the bior and rbio wavelet basis functions are better at denoising MI-EEG among the 7 types of wavelet clusters. Among them, the rbio2.2 wavelet basis is the most suitable for MI-EEG denoising. The comparison of simulation results verifies the correctness of the conclusions.
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