EEG emotion recognition model based on attention and GAN

We designed a generative adversarial network and an attention network to solve the brainwave emotion-classification problem. Using spatial attention and channel attention superposition to normalize and enhance the raw EEG data, we effectively solved the defects in EEG data with weak features and easily to be disturbed them. First, the cognitive map of the brain in the emotional state was constructed by extracting graphical features from the EEG signals. Simultaneously, generative adversarial networks are used to add noise to the cognitive map to generate similar data. The volume of the brain cognitive map has been expanded. The problem of insufficient EEG signal data was solved, and the accuracy and robustness were improved. Finally, we compared the processing ability of different neural networks on EEG and adversarial signals. Compared with other deep learning models and parameter optimization methods, the proposed model achieved a detection accuracy of 94.87% on the SEED dataset.