关键词: 小波包熵, 支持向量机, 脑电信号分类

Abstract: Aiming at the problem that difficulty of feature extraction and low recognition accuracy rate, using Wavelet Packet Entropy （WPE） to feature extraction and Support Vector Machines （SVM） to classification is proposed. First, the power of motor imagery EEG data is calculated, and the scale of wavelet packet is selected by theoretical analysis. Then, the wavelet packet decomposition on power is done, the WPE of power is caculated, and the wavelet packet entropy interpolation of C3, C4 lead is extracted, which compose feature vector. Finally, the feature vector is fed as classifier input into support vector machine to achieve classification. From Graz’s EEG data of international BCI competition 2003, the highest accuracy rate of classification is 97.56%. The feature vectors of this algorithm are low in dimension and small in data size, also have high classification accuracy, which provides a reference method for the task of EEG feature extraction and classification.

Key words: wavelet packet entropy, support vector machine, classification of EEG signal