{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T06:10:16Z","timestamp":1782627016926,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>EEG-based emotion recognition (EEG-ER) through deep learning models has gained more attention in recent years, with more researchers focusing on architecture, feature extraction, and generalisability. This paper presents a novel end-to-end deep learning framework for EEG-ER, combining temporal feature extraction, self-attention mechanisms, and adversarial domain adaptation. The architecture entails a multi-stage 1D CNN for spatiotemporal features from raw EEG signals, followed by a transformer-based attention module for long-range dependencies, and a domain-adversarial neural network (DANN) module with gradient reversal to enable a powerful subject-independent generalisation by learning domain-invariant features. Experiments on benchmark datasets (DEAP, SEED, DREAMER) demonstrate that our approach achieves a state-of-the-art performance, with a significant improvement in cross-subject recognition accuracy compared to non-adaptive frameworks. The architecture tackles key challenges in EEG emotion recognition, including generalisability, inter-subject variability, and temporal dynamics modelling. The results highlight the effectiveness of combining convolutional feature learning with adversarial domain adaptation for robust EEG-ER.<\/jats:p>","DOI":"10.3390\/info16070560","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T13:06:17Z","timestamp":1751288777000},"page":"560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CMHFE-DAN: A Transformer-Based Feature Extractor with Domain Adaptation for EEG-Based Emotion Recognition"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5333-0259","authenticated-orcid":false,"given":"Manal","family":"Hilali","sequence":"first","affiliation":[{"name":"Faculty of Sciences and Techniques, Hassan First University, Settat 26000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdellah","family":"Ezzati","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Techniques, Hassan First University, Settat 26000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-9948","authenticated-orcid":false,"given":"Said","family":"Ben Alla","sequence":"additional","affiliation":[{"name":"National School for Applied Science, Hassan First University, Berrechid 26100, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep Learning for Electroencephalogram (EEG) Classification Tasks: A Review","volume":"16","author":"Craik","year":"2019","journal-title":"J. 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