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论文类型：会议论文

发表时间：2016-01-01

收录刊物：CPCI-S、SCIE

页面范围：167-172

关键字：EEG signals classification; feature extraction; feature selection; feature transformation

摘要：Feature extraction and classification for EEG signals are key technologies in medical applications. This paper proposes an efficient feature extraction framework that combines hybrid feature extraction and feature selection method. In order to fully exploit information from EEG signals, several feature extraction methods of different types are applied, which are autoregressive model, discrete wavelet transform, wavelet packet transform and sample entropy. After information fusion, feature selection methods are introduced to deal with redundant and irrelevant information, which is advantageous to classification. In this phase, global optimization strategy based on binary particle swarm optimization (BPSO) is presented to enhance the performance of feature selection. To evaluate the results of feature extraction, experiments of class separability are conducted. Classification results on EEG dataset of university of Bonn show the superiority of the proposed method.