Motor imagery-based brain-computer interface for differential diagnosis in prolonged disorders of consciousness.

Introduction: Patients with prolonged disorders of consciousness (pDoC) present significant challenges to the assessment of consciousness. This study investigated the clinical utility of motor imagery-based brain-computer interface (MI-BCI) for discriminating consciousness levels in patients with pDoC. Methods: Thirty-one pDoC patients [12 with unresponsive wakefulness syndrome (UWS) and 19 in a minimally conscious state (MCS)] underwent EEG recordings during resting state and MI-BCI training. The analysis focused on relative power spectral density across five frequency bands (delta, theta, alpha, beta, gamma) in motor imagery-related regions (frontal and parietal cortices), along with BCI performance metrics (classification accuracy and attention indices). Results: We found that MCS patients exhibited multiband neural oscillation modulation during MI-BCI tasks, including slow-wave enhancement [(delta in frontal lobes (p = 0.003); theta in frontal (p = 0.026) and parietal lobes (p < 0.001)) and fast-wave suppression (alpha in frontal (p < 0.001) and parietal lobes (p = 0.049); beta in frontal (p = 0.014) and parietal lobes (p = 0.001); gamma in parietal lobes (p = 0.023)]. In contrast, UWS patients only showed localized parietal gamma enhancement (p = 0.042). Notably, the MCS group achieved significantly higher classification accuracy (55% vs. 38%, p = 0.02), and attention indices correlated moderately with CRS-R scores across all patients (Spearman's ρ = 0.43, p = 0.02). Conclusion: The findings suggest that MI-BCI classification accuracy and attention indices may serve as auxiliary discriminators between UWS and MCS patients, with MCS patients demonstrating superior responsiveness to MI-BCI training.