ArticleFrontiers in neuroscience2023
A novel feature fusion network for multimodal emotion recognition from EEG and eye movement signals.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- ICTD: Combination of Improved CNN-Transformer and Enhanced Deep Canonical Correlation Analysis for Eye-Movement Emotion Classification.Brain sciences · 2026Article
- Cross-subject generalization for EEG emotion recognition: a review of methods, challenges, and future trends.Frontiers in computational neuroscience · 2026Review
- A game-theoretic framework for multimodal information utilization under heterogeneous processing environments in neuroscience and perception science.Frontiers in neuroscience · 2026Article
- Temporal Capsule Feature Network for Eye-Tracking Emotion Recognition.Brain sciences · 2025Article
- [A method for emotion transition recognition using cross-modal feature fusion and global perception].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Bangla Speech Emotion Recognition Using Deep Learning-Based Ensemble Learning and Feature Fusion.Journal of imaging · 2025Article
- A Comprehensive Review of Multimodal Emotion Recognition: Techniques, Challenges, and Future Directions.Biomimetics (Basel, Switzerland) · 2025Review
- [Dynamic continuous emotion recognition method based on electroencephalography and eye movement signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning.PeerJ. Computer science · 2025Article
- Editorial: Integrating multimodal approaches to unravel neural mechanisms of learning and cognition.Frontiers in neurologyArticle
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotion recognition is a challenging task, and the use of multimodal fusion methods for emotion recognition has become a trend. Fusion vectors can provide a more comprehensive representation of changes in the subject's emotional state, leading to more accurate emotion recognition results. Different fusion inputs or feature fusion methods have varying effects on the final fusion outcome. In this paper, we propose a novel Multimodal Feature Fusion Neural Network model (MFFNN) that effectively extracts complementary information from eye movement signals and performs feature fusion with EEG signals. We construct a dual-branch feature extraction module to extract features from both modalities while ensuring temporal alignment. A multi-scale feature fusion module is introduced, which utilizes cross-channel soft attention to adaptively select information from different spatial scales, enabling the acquisition of features at different spatial scales for effective fusion. We conduct experiments on the publicly available SEED-IV dataset, and our model achieves an accuracy of 87.32% in recognizing four emotions (happiness, sadness, fear, and neutrality). The results demonstrate that the proposed model can better explore complementary information from EEG and eye movement signals, thereby improving accuracy, and stability in emotion recognition.
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