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Phys. Eng. Express"],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Electroencephalography (EEG) and electromyography (EMG) are widely used for decoding motor intentions, yet unimodal approaches often suffer from low robustness and limited representational information. EEG\u2013EMG hybrid brain\u2013computer interfaces (BCIs) can bridge cortical intention and muscular execution, but challenges remain in signal alignment, fusion modeling, and clinical generalization.\n                    <jats:italic>Approach<\/jats:italic>\n                    . To address these issues, we propose spatio-temporal cross-attention fusion (STCAFusion), a spatiotemporal cross-attention (CA) framework that integrates EEG and EMG through multi-band based dual-branch convolutional encoders and parallel temporal and spatial CA modules. This design enables detailed modeling of inter-modal correlations across both time and space. We evaluate STCAFusion on a newly collected dataset of synchronous EEG\u2013EMG recordings from 12 subjects, where the data were acquired under two paradigms (Reaching and Lifting) designed from daily functional upper-limb activities to emphasize directional and strength control.\n                    <jats:italic>Main results<\/jats:italic>\n                    . With leave-one-run-out cross-validation, STCAFusion achieves average accuracies of 84.15% and 95.22% in the two paradigms, outperforming the strongest competing EEG\u2013EMG fusion baselines by 3.4% in the Reaching paradigm and 1.8% in the Lifting paradigm. Visualization of learned attention weights further reveals meaningful spatiotemporal EEG\u2013EMG coupling patterns, offering insights into neural\u2013muscular coordination patterns relevant to rehabilitation-oriented BCI design.\n                    <jats:italic>Significance<\/jats:italic>\n                    . These results highlight the potential of CA-based multimodal physiological signal fusion in building reliable hybrid BCI and wearable devices for upper-limb control and rehabilitation.\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/ae802c","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T22:47:57Z","timestamp":1782168477000},"page":"045019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["EEG\u2013EMG spatiotemporal cross-attention fusion network for functional upper-limb movement classification"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2221-6350","authenticated-orcid":true,"given":"Zhonghao","family":"Yao","sequence":"first","affiliation":[{"name":"Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9299-3251","authenticated-orcid":true,"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongfei","family":"Bai","sequence":"additional","affiliation":[{"name":"Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfei","family":"Ji","sequence":"additional","affiliation":[{"name":"Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingjing","family":"Jin","sequence":"additional","affiliation":[{"name":"Tongji University"},{"name":"Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"bpexae802cbib1","doi-asserted-by":"publisher","first-page":"143","DOI":"10.5772\/58874","type":"journal-article","volume":"11","author":"Ripel","year":"2014","journal-title":"Int, J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-03-11","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-06-22","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-07-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}