ArticleBrain sciences2024
EEG-fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network.
Article in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Spatial-Frequency Hypergraph Neural Network for EEG-fNIRS Emotion Recognition.Brain sciences · 2026Article
- MSA-CNN: A Multi-Scale Attention Convolutional Neural Network for fNIRS-Based Emotion Recognition.Biosensors · 2026Article
- [Application and perspective of novel auditory intervention paradigms based on verbal and nonverbal stimuli for severe traumatic brain injury].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Article
- Decoupled Bidirectional Spatio-Temporal Fusion Network for Hybrid EEG-fNIRS Cognitive Task Classification.Brain sciences · 2026Article
- EEG-fNIRS Cross-Subject Emotion Recognition Based on Attention Graph Isomorphism Network and Contrastive Learning.Brain sciences · 2026Article
- EEG-fNIRS self-supervised emotion recognition method based on dual-stream Siamese network.Frontiers in neuroinformatics · 2026Article
- TopoTempNet: A High-Accuracy and Interpretable Decoding Method for fNIRS-Based Motor Imagery.Sensors (Basel, Switzerland) · 2025Article
- A review of hybrid EEG-based multimodal human-computer interfaces using deep learning: applications, advances, and challenges.Biomedical engineering letters · 2025Review
- Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.PloS one · 2025Article
- Emotion Recognition Based on a EEG-fNIRS Hybrid Brain Network in the Source Space.Brain sciences · 2024Article
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3 authors.
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Abstract
Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person's emotional state and have been widely studied in emotion recognition. However, the effective feature fusion and discriminative feature learning from EEG-fNIRS data is challenging. In order to improve the accuracy of emotion recognition, a graph convolution and capsule attention network model (GCN-CA-CapsNet) is proposed. Firstly, EEG-fNIRS signals are collected from 50 subjects induced by emotional video clips. And then, the features of the EEG and fNIRS are extracted; the EEG-fNIRS features are fused to generate higher-quality primary capsules by graph convolution with the Pearson correlation adjacency matrix. Finally, the capsule attention module is introduced to assign different weights to the primary capsules, and higher-quality primary capsules are selected to generate better classification capsules in the dynamic routing mechanism. We validate the efficacy of the proposed method on our emotional EEG-fNIRS dataset with an ablation study. Extensive experiments demonstrate that the proposed GCN-CA-CapsNet method achieves a more satisfactory performance against the state-of-the-art methods, and the average accuracy can increase by 3-11%.
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