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However, effectively capturing local and global dependencies remains a challenge due to the complexities of EEG data. Furthermore, traditional convolutional neural networks and RNNs often struggle to fully explore the spatio-temporal relationships between different features. To address these issues, we propose an end-to-end model with the augmented capsule-gated Transformer to improve the performance of EEG emotion recognition, in which we learn cross-channel spatial features effectively, and the raw EEG signals are automatically weighted to emphasize key attributes. Subsequently, the capsule network extracts low-level and high-level spatial information, fully leveraging the potential insights within the signals. Building on this, an efficient Transformer is employed to model the relationships among different electrodes, allowing for a more in-depth analysis of the temporal dependencies across multiple features. Extensive experiments are conducted on the Dataset for Emotion Analysis using Physiological Signals (DEAP) dataset, and comparison results with existing state-of-the-art methods demonstrate the superior performance of the proposed method. Specifically, for the arousal and valence dimensions, the average recognition accuracies in subject-dependent experiments reach 93.51% and 94.24%, while the subject-independent experiments achieve average accuracies of 86.78% and 87.59%.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaf119","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T12:14:46Z","timestamp":1758716086000},"page":"346-356","source":"Crossref","is-referenced-by-count":1,"title":["ECaps-GTR: optimizing spatiotemporal EEG emotion recognition via the augmented capsule-gated transformer"],"prefix":"10.1093","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5479-5994","authenticated-orcid":false,"given":"Xiaoliang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science , Hunan University of Science and Technology, Taoyuan Road, Yuhu District, Xiangtan 411201,","place":["China"]},{"name":"Sanya Research Institute , Hunan University of Science and Technology, Yazhou Bay Science and Technology City, Sanya 572024, Hainan Province,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8126-8625","authenticated-orcid":false,"given":"Huijing","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science , Hunan University of Science and Technology, Taoyuan Road, Yuhu District, Xiangtan 411201,","place":["China"]},{"name":"Sanya Research Institute , Hunan University of Science and Technology, Yazhou Bay Science and Technology City, Sanya 572024, Hainan Province,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3203-2173","authenticated-orcid":false,"given":"Yuzhen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science , Hunan University of Science and Technology, Taoyuan Road, Yuhu District, Xiangtan 411201,","place":["China"]},{"name":"Sanya Research Institute , Hunan University of Science and Technology, Yazhou Bay Science and Technology City, Sanya 572024, Hainan Province,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0057-7483","authenticated-orcid":false,"given":"Shuangyan","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science , Hunan University of Science and Technology, Taoyuan Road, Yuhu District, Xiangtan 411201,","place":["China"]},{"name":"Sanya Research Institute , Hunan University of Science and Technology, Yazhou Bay Science and Technology City, Sanya 572024, Hainan Province,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1381-4364","authenticated-orcid":false,"given":"Kuanching","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science , Hunan University of Science and Technology, Taoyuan Road, Yuhu District, Xiangtan 411201,","place":["China"]},{"name":"Sanya Research Institute , Hunan University of Science and Technology, Yazhou Bay Science and Technology City, Sanya 572024, Hainan Province,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1946-0384","authenticated-orcid":false,"given":"Mirjana","family":"Ivanovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Sciences , University of Novi Sad, Trg Dositeja Obradovi\u0107a 3, Novi Sad 21000,","place":["Serbia"]}]}],"member":"286","published-online":{"date-parts":[[2025,10,18]]},"reference":[{"key":"2026021823461626800_ref1","first-page":"1","article-title":"EEG based emotion recognition: a tutorial and review","volume":"55","author":"Li","year":"2022","journal-title":"ACM Comput Surv"},{"key":"2026021823461626800_ref2","doi-asserted-by":"publisher","first-page":"3525","DOI":"10.1109\/TIFS.2024.3364370","article-title":"MC-DSC: a dynamic secure resource configuration scheme based on medical consortium blockchain","volume":"19","author":"Liang","year":"2024","journal-title":"IEEE Trans Inf Forensics 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