MEEG and AT-DGNN: Advancing EEG Emotion Recognition with Music and Graph Learning

• URL: http://arxiv.org/abs/2407.05550v1
• Date: Mon, 8 Jul 2024 01:58:48 GMT
• Title: MEEG and AT-DGNN: Advancing EEG Emotion Recognition with Music and Graph Learning
• Authors: Minghao Xiao, Zhengxi Zhu, Wenyu Wang, Meixia Qu,
• Abstract summary: We introduce the MEEG dataset, a comprehensive multi-modal music-induced electroencephalogram (EEG) dataset and a novel framework for EEG-based emotion recognition. The MEEG dataset captures a wide range of emotional responses to music, enabling an in-depth analysis of patterns in musical contexts. The AT-DGNN combines an attention-based temporal learner with a dynamic graph neural network (DGNN) to accurately model the local and global graph dynamics of EEG data.
• Score: 5.766036473197785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in neuroscience have elucidated the crucial role of coordinated brain region activities during cognitive tasks. To explore the complexity, we introduce the MEEG dataset, a comprehensive multi-modal music-induced electroencephalogram (EEG) dataset and the Attention-based Temporal Learner with Dynamic Graph Neural Network (AT-DGNN), a novel framework for EEG-based emotion recognition. The MEEG dataset captures a wide range of emotional responses to music, enabling an in-depth analysis of brainwave patterns in musical contexts. The AT-DGNN combines an attention-based temporal learner with a dynamic graph neural network (DGNN) to accurately model the local and global graph dynamics of EEG data across varying brain network topology. Our evaluations show that AT-DGNN achieves superior performance, with an accuracy (ACC) of 83.06\% in arousal and 85.31\% in valence, outperforming state-of-the-art (SOTA) methods on the MEEG dataset. Comparative analyses with traditional datasets like DEAP highlight the effectiveness of our approach and underscore the potential of music as a powerful medium for emotion induction. This study not only advances our understanding of the brain emotional processing, but also enhances the accuracy of emotion recognition technologies in brain-computer interfaces (BCI), leveraging both graph-based learning and the emotional impact of music. The source code and dataset are available at \textit{https://github.com/xmh1011/AT-DGNN}.

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