Temporal GCN-based emotional recognition with EEG series in graduate employment pressure music therapy

Abstract

As employment pressure increases, university students face significant employment-related psychological stress. Music therapy is widely used to alleviate and adjust employment anxiety. However, accurately identifying and assessing the impact of music therapy on emotional states through electroencephalogram (EEG) sequence analysis poses a challenge due to the high dimensionality of EEG data and the complexity of time series. To address these issues, we propose an innovative Temporal Graph Convolutional Network (T-GCN) architecture that captures features in EEG data. Our method achieves a 93.52% Accuracy on the SEED dataset, validating the effectiveness of T-GCN and training strategy, and further supporting the feasibility of music therapy in relieving employment psychological barriers among college students, potentially informing personalized design for employment psychology adjustment and music therapy interventions.