关键词: 情感识别, 短时傅里叶变换（STFT）, 三维数据, 卷积神经网络（CNN）, 双向门控循环单元（BGRU）

Abstract:

To solve the problem of low emotional recognition rate of EEG signals, this paper proposes an EEG emotion recognition method based on 3DC-BGRU. Firstly, the Short-Time Fourier Transform（STFT） is performed on the single-channel EEG signal, and the time-frequency information of the relevant frequency band is extracted to form a two-dimensional time-frequency map, the time-frequency map of multiple channels constitutes a new three-dimensional data form of time, frequency and channels. Then, a novel Convolutional Neural Network（CNN） model is designed to extract deep features of 3D data by 3D convolution. Finally, the Bidirectional Gated Recurrent Unit（BGRU） is designed to process the sequence information of deep features and classify them with Softmax. The experimental results show that the classification recognition rate of the method is improved.

Key words: emotion recognition, Short-Time Fourier Transform（STFT）, three-dimensional data, Convolutional Neural Network（CNN）, Bidirectional Gated Recurrent Unit（BGRU）