关键词: 脑电信号, 梅尔倒谱系数（MFCC）, 特征融合, 深度残差网络

Abstract:

In recent years, more and more researchers pay attention to emotional recognition by analyzing EEG signals. In order to enrich the feature representation and obtain a higher classification accuracy of emotional recognition, this paper applies MFCC, a speech cepstrum feature, to EEG signals. After wavelet transform of EEG signal, MFCC feature and EEG feature are extracted and fused. Affective classification and recognition are carried out by using the characteristics of ResNet18. The experimental results show that, compared with traditional EEG features, the addition of MFCC features improves the recognition accuracy of emotional dimension Arousal and Valence by 6% and 4%, respectively, reaching 86.01% and 85.46%, thus improving the recognition accuracy of emotional recognition.

Key words: EEG signal, Mel-scale Frequency Cepstral Coefficients（MFCC）, feature fusion, deep residual network