BRAINet: a brain-region-aware interaction network for EEG-based diagnosis of disorders of consciousness

Abstract Objective. 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 − ), and minimally conscious state plus (MCS + ) using resting-state EEG signals. Approach. 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 − , and 15 MCS + 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. Main results. 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 p = 0.005   13 ). Significance. These results suggest that brain-region-aware EEG modeling may provide useful information for fine-grained UWS/MCS − /MCS + 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.