ArticleFrontiers in human neuroscience2023
STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition.
Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- A Convolutional Neural Network Combining Multi-scale Convolution and Functional Connectivity for Predicting rTMS Efficacy in MUD.Annals of biomedical engineering · 2026Article
- Bridging Neural Topology and Affective Computing: Graph Attention for EEG Emotion Recognition.Journal of medical systems · 2026Review
- TSCL-LwF: A Cross-Subject Emotion Recognition Model via Multi-Scale CNN and Incremental Learning Strategy.Brain sciences · 2026Article
- Cross-subject generalization for EEG emotion recognition: a review of methods, challenges, and future trends.Frontiers in computational neuroscience · 2026Review
- Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.Sensors (Basel, Switzerland) · 2025Review
- Exploring physical and functional EEG connectivity with multilayer graph transformer convolutional networks for emotion recognition.Frontiers in human neuroscience · 2025Article
- Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Review of EEG Affective Recognition with a Neuroscience Perspective.Brain sciences · 2024Review
- [Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition.Frontiers in neuroscience · 2024Article
- Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization.Frontiers in human neuroscience · 2024Article
- A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications.PeerJ. Computer science · 2024Article
- Automatic Seizure Detection Based on Stockwell Transform and Transformer.Sensors (Basel, Switzerland) · 2023Article
- EEG Emotion Recognition by Fusion of Multi-Scale Features.Brain sciences · 2023Article
- An innovative brain-inspired neuronal competition model for electroencephalography emotion recognition.Digital healthArticle
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5 authors.
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Abstract
Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience research. In this paper, we introduce a novel graph neural network called the spatial-temporal graph attention network with a transformer encoder (STGATE) to learn graph representations of emotion EEG signals and improve emotion recognition performance. In STGATE, a transformer-encoder is applied for capturing time-frequency features which are fed into a spatial-temporal graph attention for emotion classification. Using a dynamic adjacency matrix, the proposed STGATE adaptively learns intrinsic connections between different EEG channels. To evaluate the cross-subject emotion recognition performance, leave-one-subject-out experiments are carried out on three public emotion recognition datasets, i.e., SEED, SEED-IV, and DREAMER. The proposed STGATE model achieved a state-of-the-art EEG-based emotion recognition performance accuracy of 90.37% in SEED, 76.43% in SEED-IV, and 76.35% in DREAMER dataset, respectively. The experiments demonstrated the effectiveness of the proposed STGATE model for cross-subject EEG emotion recognition and its potential for graph-based neuroscience research.
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