Bridging Neural Topology and Affective Computing: Graph Attention for EEG Emotion Recognition.
Electroencephalography (EEG) offers high temporal resolution and strong physiological validity for emotion recognition. However, complex spatial organization and inter-subject variability present major modeling challenges. Graph-based spatial attention mechanisms have emerged as a key solution, pres...
| Publicado en: | Journal of Medical Systems Vol. 50; no. 1; pp. 1 - 22 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
2/20/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191837190&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191837190 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2/20/2026 vid: 50 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191837190 191837190 191837190 10.1007/s10916-026-02345-w 191837190 ppf: 1 ppct: 21 formats: tig: atl: Bridging Neural Topology and Affective Computing: Graph Attention for EEG Emotion Recognition. aug: au: Yang, Wenyang Yuan, Jingrui Duan, Bingnan Chow, Steven Kwok Keung affil: https://ror.org/040c7js64 School of Computer Science, Xi'an Shiyou University, 710065, Xi'an, P.R. China sug: subj: Electroencephalography Emotions Convolutional Neural Networks Attention Computing Methodologies Models, Theoretical Spatial Perception Reproducibility of Results Checklists ab: Electroencephalography (EEG) offers high temporal resolution and strong physiological validity for emotion recognition. However, complex spatial organization and inter-subject variability present major modeling challenges. Graph-based spatial attention mechanisms have emerged as a key solution, preserving brain topological priors while adaptively emphasizing emotion-relevant regions and connections. This review summarizes advances in graph convolutional networks (GCN) and graph attention networks (GAT), covering representative studies under both subject-dependent and subject-independent settings. In architectural innovations, this paper critically evaluates the implicit impact of experimental factors, including preprocessing pipelines and validation protocols, on performance, and proposes a standardized framework to enhance reproducibility. Existing research demonstrates progressive transitions from static to dynamic graphs and from single-domain to multimodal fusion guided by physiological priors. Future research is expected to focus on enhancing model efficiency, strengthening neurophysiological alignment, integrating multimodal information and enhancing subject-independent generalization, and extending applications to affective neuroscience and clinical contexts. These developments collectively drive EEG-based emotion recognition toward more efficient, interpretable, and translationally valuable affective computing systems. pubtype: Academic Journal doctype: equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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