Abstract:
This paper uses EEG dataset DEAP to undergo the emotion recognition. By considering the time sequence characteristics of EEG signals, we adopt temporal convolution network (TCN) as a deep learning approach to train and identify EEG data. TCN model can not only grasp the characteristics of time series, but also retain the parallel computing property of CNN. Hence, it can improve the computational efficiency, while maintaining high identification accuracy. Firstly, wavelet packet transform is employed to decompose the raw signals into sub-frequency bands, and then, wavelet coefficient energy values are computed as the features. Wavelet packet analysis is an extension of wavelet analysis, whose basic idea is to concentrate the information energy, find the order in the details, and filter out the rules. Hence, it can provide a more sophisticated analysis method for the signal. Besides, the extracted features are trained through the TCN and Snapshot optimization idea is adopted for saving multiple models during the process of training. The principle of Snapshot optimization is to avoid local pole values by constantly resetting the learning rate. Finally, the ensemble learning with the voting integration strategy is implemented to combine multiple machine learning models into an integrated model such that the recognition accuracy can be improved and the robustness of the results can be enhanced. Finally, it is shown via the experimental results that the proposed method can realize the two classification and four classification identification tasks of emotions with the average recognition accuracy, 95% and 93%, respectively. Compared with similar methods, this proposed method attains significant improvement.