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

Hongwei Zhao; Jiawen Liu; Xuechun Zhu; Menglu Su; Jiadong Xu; Yayu Zhao

doi:10.1117/12.3032968

5 July 2024 Temporal GCN-based emotional recognition with EEG series in graduate employment pressure music therapy

Hongwei Zhao, Jiawen Liu, Xuechun Zhu, Menglu Su, Jiadong Xu, Yayu Zhao

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841F (2024) https://doi.org/10.1117/12.3032968
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongwei Zhao, Jiawen Liu, Xuechun Zhu, Menglu Su, Jiadong Xu, and Yayu Zhao "Temporal GCN-based emotional recognition with EEG series in graduate employment pressure music therapy", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841F (5 July 2024); https://doi.org/10.1117/12.3032968

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