| Summary: | Mental tasks, such as calculation, reasoning, motor imagery, etc., can be recognized by the pattern of electroencephalograph (EEG) signals. So EEG signal recognition plays an important role in brain-computer interaction (BCI). In this study, to enhance the ability of classifiers such as support vector machine (SVM), deep neural networks (DNN), k-nearest neighbor method (kNN), decision tree (DT), a feature extraction method is proposed using techniques of fast Fourier transform (FFT) and receiver operating characteristic (ROC) curve. In the proposed method, the raw EEG data was transformed into power spectrum of FFT at first, and then to find frequencies decided by area under curve (AUC) of ROC between the value of spectrums of different classes of metal tasks. Experiment results using benchmark data and BCI competition II data showed the effectiveness of the proposed method for all above classifiers.
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