Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation

• URL: http://arxiv.org/abs/2408.10235v2
• Date: Mon, 23 Dec 2024 20:38:19 GMT
• Title: Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation
• Authors: Yun Xiao, Yimeng Zhang, Xiaopeng Peng, Shuzheng Han, Xia Zheng, Dingyi Fang, Xiaojiang Chen,
• Abstract summary: We introduce a multi-source dynamic contrastive domain adaptation method based on differential entropy (DE) features.<n>Our model outperforms several alternative domain adaptation methods in recognition accuracy, inter-class margin, and intra-class compactness.<n>Our study also suggests greater emotional sensitivity in the frontal and parietal brain lobes.
• Score: 17.956642824289453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electroencephalography (EEG) provides reliable indications of human cognition and mental states. Accurate emotion recognition from EEG remains challenging due to signal variations among individuals and across measurement sessions. We introduce a multi-source dynamic contrastive domain adaptation method (MS-DCDA) based on differential entropy (DE) features, in which coarse-grained inter-domain and fine-grained intra-class adaptations are modeled through a multi-branch contrastive neural network and contrastive sub-domain discrepancy learning. Leveraging domain knowledge from each individual source and a complementary source ensemble, our model uses dynamically weighted learning to achieve an optimal tradeoff between domain transferability and discriminability. The proposed MS-DCDA model was evaluated using the SEED and SEED-IV datasets, achieving respectively the highest mean accuracies of $90.84\%$ and $78.49\%$ in cross-subject experiments as well as $95.82\%$ and $82.25\%$ in cross-session experiments. Our model outperforms several alternative domain adaptation methods in recognition accuracy, inter-class margin, and intra-class compactness. Our study also suggests greater emotional sensitivity in the frontal and parietal brain lobes, providing insights for mental health interventions, personalized medicine, and preventive strategies.

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