Electrical Engineering and Systems Science > Signal Processing
[Submitted on 2 Feb 2024 (v1), last revised 30 Aug 2024 (this version, v3)]
Title:Graph Neural Networks in EEG-based Emotion Recognition: A Survey
View PDF HTML (experimental)Abstract:Compared to other modalities, EEG-based emotion recognition can intuitively respond to the emotional patterns in the human brain and, therefore, has become one of the most concerning tasks in the brain-computer interfaces field. Since dependencies within brain regions are closely related to emotion, a significant trend is to develop Graph Neural Networks (GNNs) for EEG-based emotion recognition. However, brain region dependencies in emotional EEG have physiological bases that distinguish GNNs in this field from those in other time series fields. Besides, there is neither a comprehensive review nor guidance for constructing GNNs in EEG-based emotion recognition. In the survey, our categorization reveals the commonalities and differences of existing approaches under a unified framework of graph construction. We analyze and categorize methods from three stages in the framework to provide clear guidance on constructing GNNs in EEG-based emotion recognition. In addition, we discuss several open challenges and future directions, such as Temporal full-connected graph and Graph condensation.
Submission history
From: Xinliang Zhou [view email][v1] Fri, 2 Feb 2024 04:30:58 UTC (2,542 KB)
[v2] Wed, 14 Aug 2024 08:02:06 UTC (2,542 KB)
[v3] Fri, 30 Aug 2024 02:53:24 UTC (2,542 KB)
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