ArticleFrontiers in computational neuroscience2024
EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism.
Article in Frontiers in computational neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Electroencephalography-Based Emotion Recognition Using Auditory Stimulation for Affective Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Explainable Temporal Deep Learning for EEG-Based Depression Detection Using Resting-State Brain Dynamics.International journal of methods in psychiatric research · 2026Article
- Cross-domain multimodal learning for stress-level prediction: a hybrid deep learning framework integrating independent EEG and facial expression datasets.Scientific reports · 2026Article
- Bridging Neural Topology and Affective Computing: Graph Attention for EEG Emotion Recognition.Journal of medical systems · 2026Review
- Enhanced EEG Emotion Recognition Using MIMO-Based Denoising and Band-Wise Attention Graph Neural Network.Sensors (Basel, Switzerland) · 2026Article
- A dual-branch network with brain region-constrained attention for EEG emotion recognition.Frontiers in neuroscience · 2026Article
- Two-Dimensional Latent Space Manifold of Brain Connectomes Across the Spectrum of Clinical Cognitive Decline.Bioengineering (Basel, Switzerland) · 2025Article
- The cognitive impacts of large language model interactions on problem solving and decision making using EEG analysis.Frontiers in computational neuroscience · 2025Article
- Deep learning approaches for EEG-based healthcare applications: a comprehensive review.Frontiers in human neuroscience · 2025Review
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5 authors.
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Abstract
EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments' accuracy of 99.42% and subject-independent experiments' accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.