Abstract: The asymmetry of the brain will have an impact on EEG emotion analysis,but many studies have not considered this property.Combined with the asymmetry of brain space,this paper proposes a hybrid model,which uses multi-scale convolutional neural network to extract the EEG spatial features of left and right asymmetry of the brain,then uses bidirectional long short-term memory neural network to extract temporal features,and finally learns the relationship between features through the multi-head self-attention mechanism.The proposed model is experimentally validated on the publicly available DEAP dataset.The accuracy and F1-score for classifying the arousal dimension are 93.11% and 93.46%, respectively,while those for the valence dimension are 92.12% and 93.27%.Furthermore,the model is validated on the publicly available MAHNOB-HCI dataset,achieving accuracy and F1-score of 98.58% and 97.98% for the arousal dimension,and accuracy and F1-score of 98.76% and 98.25% for the valence dimension.The results demonstrate that the proposed model exhibits certain advantages in EEG-based emotion recognition.Furthermore,ablative experiments confirm the significance of the asymmetrical spatial layer.

Key words: EEG emotion recognition, asymmetric spatial feature, multi-scale convolutional neural network, bidirectional long short-term memory neural network, multi-head self-attention mechanism