| Abstrakt: |
Emotion recognition based on EEG signals has become an important challenge in the field of emotional computing and human-computer interaction. In order to obtain better emotion recognition performance, a key issue is how to effectively combine time, space and frequency dimension information in EEG signals. This paper proposes a hybrid network model (ECA-ResNet-LSTM) combining attention residual networks and long short-term memory networks. By integrating time, space and frequency information in EEG signals, this model can effectively improve the accuracy of emotion recognition. Firstly, the differential entropy features of EEG signals in different frequency bands after time-domain segmentation are extracted, and the differential entropy features extracted from different channels are transformed into a four-dimensional feature matrix. Then, the spatial and frequency information in the EEG signal is extracted through ECA ResNet, and attention mechanisms are introduced to redistribute the weights of more relevant frequency band information. LSTM extracts time related information from the output of ECA ResNet. Finally, the experimental results show that in DEAP dataset, the accuracy of awakening and valence dimension binary classification reaches 97.15% and 96.13%, respectively, and the accuracy of awakening valence dimension four classification reaches 95.96%; In SEED dataset, the accuracy of positive neutral negative three classification reaches 96.64%. Compared with the existing mainstream emotion, the classification accuracy of the recognition model has been significantly improved. [ABSTRACT FROM AUTHOR] |
