Motor Imagery EEG Feature Extraction Based on Fuzzy Entropy with Wavelet Transform

Due to the nonlinear characteristics of EEG signals and the rhythm characteristics of motor imagery, the low recognition rate of using single feature extraction algorithm, a feature extraction method based on wavelet transform and fuzzy entropy is presented in this paper. The EEG signals are decomposed to three levels by the wavelet transform, according to the ERS/ERD phenomena during motor imagery, the alpha rhythm and beta rhythm signal can be extracted by the algorithm of fuzzy entropy. Finally, the motor imagery EEG signals are classified by a support vector machine classifier. BCI Competition IV Datasets1 has been used to conduct the experiment, the experimental results show that the feature extraction method combining wavelet transform and fuzzy entropy is much better than the ways of using single fuzzy entropy, sample entropy, or others, and its highest recognition rate is 90.25%.