Research on mental disorder recognition of EEG signals based on transformer

This paper proposes a Transformer-based deep learning model for EEG analysis to achieve automatic recognition of mental disorders. First, the EEG signals of the public dataset are preprocessed and feature extracted; then a Transformer network with a self-attention mechanism is constructed, which can effectively capture the temporal characteristics and long-distance dependencies in EEG signals. Experimental results show that the proposed method outperforms traditional CNN and RNN models in terms of recognition accuracy, precision, and recall. Through channel importance analysis, it is found that specific EEG channels have significant contributions in the recognition of different mental disorders. This study provides new ideas for early auxiliary diagnosis of mental disorders, and also provides valuable technical references for the field of biological signal analysis.