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

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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Journal
Communications Medicine
Published
2026-04-17
DOI
https://doi.org/10.1038/s43856-026-01574-x
Primary Topic
Traumatic Brain Injury Research
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Krishnan Padmakumari Sivaraman Nair, José M. Sánchez‐Bornot, Kudret Yelden, Karl McCreadie et al.
Communications Medicine
Traumatic Brain Injury Research
article

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

Krishnan Padmakumari Sivaraman Nair, José M. Sánchez‐Bornot, Kudret Yelden, Karl McCreadie, Stephanie Hodge, Natalie Dayan, Abayomi Salawu, Alison McCann, Lloyd Bradley, Sheryl Parke, Simon Judge, Damon Hoad, Emma Vines, Attila Korik, Leah Hudson, Jacqueline Stow, Jacinta McElligott, Jacqueline A. Pogue, Áine M. Carroll, Naomi du Bois, Raymond Carson, Venu Harilal, Paul Johnson, Emma Dodds, Damien Coyle, Ainjila S. Elahi, Alain Bigirimana
article en

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.

Communications Medicine
University College Dublin (IE), Queen's University Belfast (GB), University of Ulster (GB), University of Hull (GB), Royal Hospital for Neuro-disability (GB), Barnsley Hospital NHS Foundation Trust (GB), Northern Health and Social Care Trust (GB), Western Health and Social Care Trust (GB), Norfolk Community Health and Care NHS Trust (GB), Sheffield Teaching Hospitals NHS Foundation Trust (GB), Oxford University Hospitals NHS Trust (GB), South Warwickshire NHS Foundation Trust (GB), King's College Hospital (GB), National Rehabilitation Hospital (IE), Bath Institute for Rheumatic Diseases (GB), University of Bath (GB)
UK Research and Innovation, National Institute for Health and Care Research, Department of Health and Social Care, University of Bath, Ulster University, Engineering and Physical Sciences Research Council, NIHR Sheffield Clinical Research Facility, NIHR Sheffield Biomedical Research Centre
Quality Education
Openalex Percentile: Top 23%
Traumatic Brain Injury Research
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