Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition

• URL: http://arxiv.org/abs/2208.11087v1
• Date: Fri, 19 Aug 2022 12:15:10 GMT
• Title: Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition
• Authors: Yiwen Zhu, Kaiyu Gan, and Zhong Yin
• Abstract summary: Technique of emotion recognition enables computers to classify human affective states into discrete categories. The emotion may fluctuate instead of maintaining a stable state even within a short time interval. There is also a difficulty to take the full use of the EEG spatial distribution due to its 3-D topology structure.
• Score: 4.331986787747648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Technique of emotion recognition enables computers to classify human affective states into discrete categories. However, the emotion may fluctuate instead of maintaining a stable state even within a short time interval. There is also a difficulty to take the full use of the EEG spatial distribution due to its 3-D topology structure. To tackle the above issues, we proposed a locally temporal-spatial pattern learning graph attention network (LTS-GAT) in the present study. In the LTS-GAT, a divide-and-conquer scheme was used to examine local information on temporal and spatial dimensions of EEG patterns based on the graph attention mechanism. A dynamical domain discriminator was added to improve the robustness against inter-individual variations of the EEG statistics to learn robust EEG feature representations across different participants. We evaluated the LTS-GAT on two public datasets for affective computing studies under individual-dependent and independent paradigms. The effectiveness of LTS-GAT model was demonstrated when compared to other existing mainstream methods. Moreover, visualization methods were used to illustrate the relations of different brain regions and emotion recognition. Meanwhile, the weights of different time segments were also visualized to investigate emotion sparsity problems.

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