A Customized ECA-CRNN Model for Emotion Recognition Based on EEG Signals

Electroencephalogram (EEG) signals are electrical signals generated by changes in brain potential. As a significant physiological signal, EEG signals have been applied in various fields, including emotion recognition. However, current deep learning methods based on EEG signals for emotion recognition lack consideration of important aspects and comprehensive analysis of feature extraction interactions. In this paper, we propose a novel model named ECA-CRNN for emotion recognition using EEG signals. Our model integrates the efficient channel attention (ECA-Net) module into our modified combination of a customized convolutional neural network (CNN) and gated circulation unit (GRU), which enables more comprehensive feature extraction, enhances the internal relationship between frequency bands and improves recognition performance. Additionally, we utilize four-dimensional data as input to our model, comprising temporal, spatial and frequency information. The test on the DEAP dataset demonstrates that it enhances the recognition accuracy of EEG signals in both arousal and valence to 95.70% and 95.33%, respectively, while also reducing the standard deviation during five-fold cross-validation to 1.16 and 1.45 for arousal and valence, respectively, surpassing most methods.

Automated summary

In this paper, a novel model named ECA-CRNN is proposed for emotion recognition using EEG signals. The model integrates the ECA-Net module into a modified CNN and GRU, allowing for more comprehensive feature extraction and improved recognition performance. The results on the DEAP dataset show that the model achieves recognition accuracies of 95.70% and 95.33% for arousal and valence, respectively, surpassing most existing methods.