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Neural Eng."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n Reliable assessment and stratification of disorders of consciousness (DOC) is essential for patient care and clinical treatment planning. Electroencephalography (EEG) provides a non-invasive approach to measure neural activity and has shown promise in DOC assessment. However, most existing EEG-based approaches focus on binary unresponsive wakefulness syndrome (UWS)\/minimally conscious state (MCS) classification and often process EEG channels as a whole, without explicitly modeling anatomical brain-region organization. In this study, our goal is to distinguish among UWS, minimally conscious state minus (MCS\n \n \n \n <\/jats:tex-math>\n \n \n \u2212<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), and minimally conscious state plus (MCS\n \n \n \n <\/jats:tex-math>\n \n \n +<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ) using resting-state EEG signals.\n Approach.<\/jats:italic>\n We propose BRAINet, a brain-region-aware EEG framework for three-class DOC classification. BRAINet partitions EEG channels into five anatomical brain regions, extracts region-specific spatiotemporal and spectral features, models cross-region interactions using a Transformer-based attention module, and fuses the learned representations with approximate entropy features for final classification. We evaluated BRAINet on a clinical resting-state EEG dataset comprising 22 UWS, 24 MCS\n \n \n \n <\/jats:tex-math>\n \n \n \u2212<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , and 15 MCS\n \n \n \n <\/jats:tex-math>\n \n \n +<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n patients using patient-wise five-fold cross-validation and comparisons with representative machine-learning and deep-learning baselines. Statistical comparisons were based on paired patient-level out-of-fold (OOF) predictions.\n Main results.<\/jats:italic>\n BRAINet achieved the highest numerical performance among the compared methods. Across the five folds, its mean balanced accuracy (BA) was 54.07% at the epoch level and 59.89% at the subject level. Based on pooled patient-level OOF predictions, BRAINet achieved significantly higher BA than the best-performing baseline, Conformer (two-sided paired permutation test, Holm-adjusted\n \n \n \n <\/jats:tex-math>\n \n \n p<\/mml:mi>\n =<\/mml:mo>\n 0.005<\/mml:mn>\n \u2009<\/mml:mtext>\n 13<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ).\n Significance.<\/jats:italic>\n These results suggest that brain-region-aware EEG modeling may provide useful information for fine-grained UWS\/MCS\n \n \n \n <\/jats:tex-math>\n \n \n \u2212<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n \/MCS\n \n \n \n <\/jats:tex-math>\n \n \n +<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n classification. BRAINet provides an interpretable framework for exploring region-specific EEG representations in DOC and may support future studies on patient stratification and prognostic assessment.\n <\/jats:p>","DOI":"10.1088\/1741-2552\/aea7db","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T22:52:57Z","timestamp":1789512777000},"page":"056041","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["BRAINet: a brain-region-aware interaction network for EEG-based diagnosis of disorders of consciousness"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8732-157X","authenticated-orcid":true,"given":"Haoxiang","family":"Chen","sequence":"first","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4628-5198","authenticated-orcid":true,"given":"Sha","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiquan","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumeng","family":"Bai","sequence":"additional","affiliation":[{"name":"Hangzhou Mingzhou Brain Rehabilitation Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijian","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9892-5778","authenticated-orcid":false,"given":"Benyan","family":"Luo","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-6181","authenticated-orcid":true,"given":"Gang","family":"Pan","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"jneaea7dbbib1","author":"Gosseries","year":"2011","type":"book"},{"key":"jneaea7dbbib2","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1038\/s41582-020-00428-x","type":"journal-article","article-title":"Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies","volume":"17","author":"Edlow","year":"2021","journal-title":"Nat. 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