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An efficient graph learning system for emotion recognition inspired by the cognitive prior graph of EEG brain network

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dc.contributor.authorLi, C.
dc.contributor.authorTang, T.
dc.contributor.authorPan, Y.
dc.contributor.authorLei, Y.
dc.contributor.authorZhang, S.
dc.contributor.authorChen, Z.
dc.contributor.authorLi, P.
dc.contributor.authorGao, D.
dc.contributor.authorChen, H.
dc.contributor.authorLi, F.
dc.contributor.authorYao, D.
dc.contributor.authorCao, Z.
dc.contributor.authorXu, P.
dc.date.issued2025
dc.description.abstractBenefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art learning strategies to automatically learn emotion-related brain cognitive patterns from emotional EEG signals, and the learned stable cognitive patterns effectively ensure the robustness of the emotion recognition system. In this work, to realize the efficient decoding of emotional EEG, we propose a graph learning system Graph Convolutional Network framework with Brain network initial inspiration and Fused attention mechanism (BF-GCN) inspired by the brain cognitive mechanism to automatically learn graph patterns from emotional EEG and improve the performance of EEG emotion recognition. In the proposed BF-GCN, three graph branches, i.e., cognition-inspired functional graph branch, data-driven graph branch, and fused common graph branch, are first elaborately designed to automatically learn emotional cognitive graph patterns from emotional EEG signals. And then, the attention mechanism is adopted to further capture the brain activation graph patterns that are related to emotion cognition to achieve an efficient representation of emotional EEG signals. Essentially, the proposed BF-CGN model is a cognition-inspired graph learning neural network model, which utilizes the spectral graph filtering theory in the automatic learning and extracting of emotional EEG graph patterns. To evaluate the performance of the BF-GCN graph learning system, we conducted subject-dependent and subject-independent experiments on two public datasets, i.e., SEED and SEED-IV. The proposed BF-GCN graph learning system has achieved 97.44% (SEED) and 89.55% (SEED-IV) in subject-dependent experiments, and the results in subject-independent experiments have achieved 92.72% (SEED) and 82.03% (SEED-IV), respectively. The state-of-the-art performance indicates that the proposed BF-GCN graph learning system has a robust performance in EEG-based emotion recognition, which provides a promising direction for affective computing.
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2025; 36(4):7130-7144
dc.identifier.doi10.1109/TNNLS.2024.3405663
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.orcidCao, Z. [0000-0003-3656-0328]
dc.identifier.urihttps://hdl.handle.net/11541.2/39055
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.fundingARC DE220100265
dc.relation.fundingSTI 2030-Major Projects 2022ZD0208500
dc.relation.fundingSTI 2030-Major Projects 2022ZD0211400
dc.relation.fundingNational Natural Science Foundation of China 62103085
dc.relation.fundingNational Natural Science Foundation of China U19A2082
dc.relation.fundingNational Natural Science Foundation of China 61961160705
dc.relation.fundingNational Natural Science Foundation of China 61901077
dc.relation.fundingNational Natural Science Foundation of China 62076209
dc.rightsCopyright 2024 IEEE
dc.source.urihttps://doi.org/10.1109/TNNLS.2024.3405663
dc.subjectaffective computing
dc.subjectcognition-inspired learning
dc.subjectelectroencephalogram (EEG) brain networks
dc.subjectemotion recognition
dc.subjectgraph neural network
dc.titleAn efficient graph learning system for emotion recognition inspired by the cognitive prior graph of EEG brain network
dc.typeJournal article
pubs.publication-statusPublished
ror.mmsid9916865928301831

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