{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T15:45:39Z","timestamp":1778427939644,"version":"3.51.4"},"reference-count":70,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["62071123"],"award-info":[{"award-number":["62071123"]}]},{"name":"the National Natural Science Foundation of China","award":["2024J01971"],"award-info":[{"award-number":["2024J01971"]}]},{"name":"the National Natural Science Foundation of China","award":["FJHJF-L-2019-7"],"award-info":[{"award-number":["FJHJF-L-2019-7"]}]},{"name":"the Natural Science Foundation of Fujian Province","award":["62071123"],"award-info":[{"award-number":["62071123"]}]},{"name":"the Natural Science Foundation of Fujian Province","award":["2024J01971"],"award-info":[{"award-number":["2024J01971"]}]},{"name":"the Natural Science Foundation of Fujian Province","award":["FJHJF-L-2019-7"],"award-info":[{"award-number":["FJHJF-L-2019-7"]}]},{"name":"Fujian Province Marine Economy Development Subsidy Fund Project","award":["62071123"],"award-info":[{"award-number":["62071123"]}]},{"name":"Fujian Province Marine Economy Development Subsidy Fund Project","award":["2024J01971"],"award-info":[{"award-number":["2024J01971"]}]},{"name":"Fujian Province Marine Economy Development Subsidy Fund Project","award":["FJHJF-L-2019-7"],"award-info":[{"award-number":["FJHJF-L-2019-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Recent advancements in decoding electroencephalogram (EEG) signals for motor imagery tasks have shown significant potential. However, the intricate time\u2013frequency dynamics and inter-channel redundancy of EEG signals remain key challenges, often limiting the effectiveness of single-scale feature extraction methods. To address this issue, we propose the Dual-Branch Blocked-Integration Self-Attention Network (DB-BISAN), a novel deep learning framework for EEG motor imagery classification. The proposed method includes a Dual-Branch Feature Extraction Module designed to capture both temporal features and spatial patterns across different scales. Additionally, a novel Blocked-Integration Self-Attention Mechanism is employed to selectively highlight important features while minimizing the impact of redundant information. The experimental results show that DB-BISAN achieves state-of-the-art performance. Also, ablation studies confirm that the Dual-Branch Feature Extraction and Blocked-Integration Self-Attention Mechanism are critical to the model\u2019s performance. Our approach offers an effective solution for motor imagery decoding, with significant potential for the development of efficient and accurate brain\u2013computer interfaces.<\/jats:p>","DOI":"10.3390\/info16070582","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T06:03:13Z","timestamp":1751868193000},"page":"582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Deep Learning Model for Motor Imagery Classification in Brain\u2013Computer Interfaces"],"prefix":"10.3390","volume":"16","author":[{"given":"Wenhui","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6161-2954","authenticated-orcid":false,"given":"Shunwu","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"given":"Qingqing","family":"Hu","sequence":"additional","affiliation":[{"name":"Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China"}]},{"given":"Yiran","family":"Peng","sequence":"additional","affiliation":[{"name":"Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"given":"Zhaowen","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"},{"name":"Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14681","DOI":"10.1007\/s00521-021-06352-5","article-title":"Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review","volume":"35","author":"Altaheri","year":"2023","journal-title":"Neural Comput. 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