{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:49:23Z","timestamp":1786121363375,"version":"build-2736575974"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171123"],"award-info":[{"award-number":["62171123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62171123"],"award-info":[{"award-number":["62171123"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2023YFC3603600"],"award-info":[{"award-number":["2023YFC3603600"]}]},{"DOI":"10.13039\/501100012216","name":"Research Start-up Funding for High-level Talent of Jiangsu University of Science and Technology","doi-asserted-by":"publisher","award":["1132932502"],"award-info":[{"award-number":["1132932502"]}],"id":[{"id":"10.13039\/501100012216","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012216","name":"Research Start-up Funding for High-level Talent of Jiangsu University of Science and Technology","doi-asserted-by":"publisher","award":["1132932304"],"award-info":[{"award-number":["1132932304"]}],"id":[{"id":"10.13039\/501100012216","id-type":"DOI","asserted-by":"publisher"}]},{"award":["1132932502"],"award-info":[{"award-number":["1132932502"]}],"id":[{"id":"https:\/\/ror.org\/00tyjp878","id-type":"ROR","asserted-by":"publisher"}]},{"award":["1132932304"],"award-info":[{"award-number":["1132932304"]}],"id":[{"id":"https:\/\/ror.org\/00tyjp878","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Electroencephalography (EEG)-based emotion recognition is an important biosensing technique for affective brain-computer interfaces (BCIs), mental-state assessment, and physiological monitoring. Existing methods often rely on a single spectral descriptor or regular two-dimensional brain maps, which makes it difficult to jointly model local spatial representations, global spatial relations, and temporal dynamics. This paper proposes GLSTNet, a global-local spatial relations and temporal dynamics network for EEG emotion recognition. EEG trials are divided into short windows, from which multi-band spectral features are extracted and arranged into compact spatial maps. The local spatial encoder (LSE) learns local spatial and spatial\u2013spectral representations from these compact multi-band spatial maps. The global spatial-relation encoder (GSRE) models long-range spatial relations between non-adjacent electrodes using a Pearson correlation prior and a learnable residual adjacency matrix. After local and global representations are integrated through gated fusion, the temporal dynamics encoder (TDE) models consecutive EEG windows using a gated recurrent unit with temporal attention. Comprehensive validation is conducted on two public EEG emotion datasets, the Database for Emotion Analysis using Physiological Signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). In the subject-dependence setting, GLSTNet achieves 93.50 \u00b1 3.22% accuracy for valence and 93.79 \u00b1 3.64% accuracy for arousal on DEAP, and 92.48 \u00b1 3.30% accuracy on SEED. In the subject-independence setting with target-subject calibration, GLSTNet obtains 75.61 \u00b1 6.19% and 79.57 \u00b1 5.99% accuracy for DEAP valence and arousal, respectively, and 88.22 \u00b1 4.70% accuracy on SEED. These results indicate that integrating global-local spatial relations with temporal dynamics provides an effective representation strategy for EEG-based emotion recognition.<\/jats:p>","DOI":"10.3390\/bios16080421","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:14:46Z","timestamp":1785917686000},"page":"421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GLSTNet: A Global-Local Spatial Relations and Temporal Dynamics Network for EEG-Based Emotion Recognition"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3585-7843","authenticated-orcid":false,"given":"Ran","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meiyu","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiyun","family":"Ma","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengyu","family":"Liu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1146\/annurev.psych.56.091103.070234","article-title":"Emotion and cognition: Insights from studies of the human amygdala","volume":"57","author":"Phelps","year":"2006","journal-title":"Annu. 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