Emotion detection unveiled: A cognitive–computational synthesis of physiological models, machine learning, and datasets.
This comprehensive survey synthesizes state-of-the-art advancements in emotion recognition based on physiological signals, specifically focusing on the paradigm shift occurring between 2021 and 2025. Crucially, we move beyond a technical review by establishing a novel Cognitive–Computational Synthes...
| Publicado en: | Cognitive, Affective & Behavioral Neuroscience Vol. 26; no. 4; pp. 1437 - 1452 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2026
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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=195499792&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195499792 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15307026 NA4 jtl: Cognitive, Affective & Behavioral Neuroscience issn: 15307026 maglogo: N pubinfo: dt: Aug2026 vid: 26 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 195499792 192501800 10.3758/s13415-026-01418-0 195499792 ppf: 1437 ppct: 15 formats: tig: atl: Emotion detection unveiled: A cognitive–computational synthesis of physiological models, machine learning, and datasets. aug: au: Machhi, Vilas Shah, Apurva affil: https://ror.org/01bx8ja67 Computer Science & Engineering Department, The MS University of Baroda, 390001, Vadodara, Gujarat, India sug: ab: This comprehensive survey synthesizes state-of-the-art advancements in emotion recognition based on physiological signals, specifically focusing on the paradigm shift occurring between 2021 and 2025. Crucially, we move beyond a technical review by establishing a novel Cognitive–Computational Synthesis Framework (CCSF). This framework explicitly maps multimodal physiological manifestations (e.g., electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR)) to underlying cognitive processes, such as attentional allocation, arousal regulation, and perceptual bias, providing a theoretical foundation for explainable AI (XAI) in affective computing. We meticulously examine the transition from traditional machine learning to advanced deep learning architectures, highlighting how recent innovations in Transformers, self-supervised learning, and diffusion models have shattered previous performance plateaus. While earlier dimensional models were often limited to 70–75% accuracy, this survey details how modern architectures now achieve benchmarks exceeding 95% on seminal datasets like SEED and DREAMER. Furthermore, the survey provides a rigorous analysis of 40 key studies (identified via PRISMA protocols), evaluating them based on their validation strategies, cross-subject generalizability, and adversarial robustness. By bridging the gap between raw physiological data and cognitive theory, this work offers a strategic roadmap for the next generation of robust, interpretable, and real-time emotion recognition systems. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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