MSFR-GCN: A Multi-Scale Feature Reconstruction Graph Convolutional Network for EEG Emotion and Cognition Recognition

Pan, Deng; Zheng, Haohao; Feifan, Xu,; Ouyang, Yu; Jiang, Zhe; Wang, Chu; Zeng, Hong

doi:10.1109/tnsre.2023.3304660

Cited by 9 publications

(1 citation statement)

References 38 publications

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“…At the same time, electroencephalography (EEG), as an intuitive biological signal reflecting brain activity, occupies a place in the field of emotion monitoring due to its rich emotional encoding potential. EEG can provide real-time and continuous monitoring of emotional states, providing a new perspective for emotion recognition and analysis [1] [2].…”

Section: Introductionmentioning

confidence: 99%

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

ZHAO,

LIU,

ZHU

et al. 2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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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.

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Section: Introductionmentioning

confidence: 99%