Assess Consciousness from Long-Duration EEG Monitoring via Evidence Accumulation Transformer

Assessing consciousness in patients with severe disorders of consciousness plays a critical role in diagnosis, prognosis, rehabilitation planning, and clinical decision-making. Reliable EEG-based assessment requires long-duration monitoring to capture consciousness-related patterns that may be sparse, intermittent, and evolving. Identifying nuanced signatures of consciousness from long-duration EEG remains challenging because evidence must be localized and accumulated across the recording, while supervision is available only through subject-level diagnoses. We propose EviFormer, the Evidence Accumulation Transformer, to assess consciousness from long-duration EEG. EviAttention applies attention along time and across electrodes, then models temporal patterns of different durations through a mixture of experts. EviMemory uses gated causal convolutions to accumulate recurring evidence across EEG chunks. EviLearning supervises subject-level evidence seeking by learning representations over evidence sets. We collect a sleep EEG dataset involving 140 subjects spanning different levels of consciousness, including 60 subjects diagnosed with disorders of consciousness. Across the collected cohorts and public datasets, EviFormer achieves state-of-the-art or competitive performance, while attribution analyses highlight delta-band and temporal evidence together with long-range anterior–posterior and bilateral electrode patterns.