What question did this study set out to answer?

The aim is to evaluate an EEG-based Brain-Computer Interface to better assess awareness in patients with Prolonged Disorders of Consciousness.

April 19, 2026Open Access

Advancing EEG-based assessment of consciousness and cognition in prolonged disorders of consciousness

Key Points

The aim is to evaluate an EEG-based Brain-Computer Interface to better assess awareness in patients with Prolonged Disorders of Consciousness.
Forty-four participants completed imagined-movement tasks using wearable EEG.
The protocol included training with neurofeedback and assessment of sensorimotor rhythm modulation.
Standard behavioral assessments were administered to compare results.
73.8% of patients achieved significant Brain-Computer Interface decoding accuracy.
LIS patients outperformed MCS and UWS patients in MI-BCI tasks.
Combining EEG predictions with behavioral assessments improved diagnostic accuracy to 62%.

Abstract

Abstract Background Accurate assessment of residual awareness in patients with Prolonged Disorders of Consciousness (PDoC) remains a major clinical challenge, as conventional behavioural tools can underestimate covert cognition. This study evaluates whether a structured, multi-phase motor imagery Brain–Computer Interface (MI-BCI) protocol provides objective electroencephalography (EEG)-based indicators of awareness that complement behavioural assessments. Methods Forty-four participants ( N = 44) completed repeated imagined-movement tasks using wearable EEG (PDoC: Unresponsive Wakefulness Syndrome (UWS, n = 14), Minimally Conscious State (MCS, n = 17), Locked-In Syndrome (LIS, n = 11); two able-bodied participants as benchmarks; ClinicalTrials.gov: NCT03827187; 30-01-2019). The protocol assessed sensorimotor rhythm modulation, training with and without neurofeedback, and binary question answering across phases. Standard behavioural assessments (CRS-R and WHIM) were administered at each session. Results Significant MI-BCI decoding accuracy (DA) is achieved by 73.8% of patients, of whom 90% progress to Q&A testing and frequently exceed the 70% usability threshold, revealing marked inter-individual heterogeneity. For significant MI-BCI runs, LIS outperform MCS ( p = 0.007) and UWS ( p = 0.048), while UWS exceed MCS during Q&A ( p = 0.049), driven by familiar-voice stimuli. Using leave-one-subject-out cross-validation, combining predictions from DA and behavioural assessments improves balanced diagnostic accuracy to 62% (from 55%), increasing sensitivity to MCS (39% to 69%), with a modest reduction in LIS sensitivity (78% to 67%). Task-related activity over sensorimotor and parietal cortices differentiate diagnostic groups. Conclusions The structured MI-BCI protocol demonstrates potential as a movement-independent, EEG-based tool for distinguishing UWS, MCS and LIS. Integrating DA and spatial patterns yields diagnostic information that may augment behavioural assessment and advance objective tools for evaluating awareness in PDoC.

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