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

du Bois, Naomi, Korik, Attila, Hodge, Stephanie, Hudson, Leah, Elahi, Ainjila S., Bigirimana, Alain, Dayan, Natalie, Sanchez-Bornot, Jose M., McCann, Alison, Yelden, Kudret, Bradley, Lloyd, Nair, Krishnan P. S., Judge, Simon, Hoad, Damon, Vines, Emma, Harilal, Venu, Parke, Sheryl, Johnson, Paul, Pogue, Jacqueline, Dodds, Emma, Salawu, Abayomi, Carson, Raymond, McCreadie, Karl, Stow, Jacqueline, McElligott, Jacinta, Carroll, Aine, Coyle, Damien·2026·Communications Medicine
DOI 10.1038/s43856-026-01574-x
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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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  1. Cavanna, A. E., Shah, S., Eddy, C. M., Williams, A. & Rickards, H. Consciousness: a neurological perspective. Behav. Neurol. 24, 107–116 (2011). · DOI
  2. Jöhr, J., Pignat, J. M. & Diserens, K. Neurobehavioural evaluation of disorders of consciousness. Swiss Arch. Neurol. Psychiatry Psychother. 166, 163–169 (2015).
  3. León-Carrión, J., Van Eeckhout, P. & Domínguez-Morales, M. D. R. Review of subject: The locked-in syndrome: a syndrome looking for a therapy. Brain Inj 16, 555–569 (2002). · DOI
  4. Heilinger, A. et al. Performance differences using a vibro-tactile P300 BCI in LIS-patients diagnosed with stroke and ALS. Front. Neurosci. 12, 1–10 (2018). · DOI
  5. Lugo, Z. R. et al. Mental imagery for brain-computer interface control and communication in non-responsive individuals. Ann. Phys. Rehabil. Med. 63, 21–27 (2020). · DOI
  6. Seel, R. T. et al. Assessment scales for disorders of consciousness: evidence-based recommendations for clinical practice and research. Arch. Phys. Med. Rehabil. 91, 1795–1813 (2010). · DOI
  7. Gill-Thwaites, H. Lotteries, loopholes and luck: misdiagnosis in the vegetative state patient. Brain Inj. 20, 1321–1328 (2006). · DOI
  8. Erp, V. W. S., Lavrijsen, J. C. M. & Vos, P. E. The vegetative state: prevalence, misdiagnosis, and treatment limitations. J. Am. Med. Dir. Assoc. 16, e9–85.e14 (2015).
  9. Wang, F. et al. Detecting brain activity following a verbal command in patients with disorders of consciousness. Front. Neurosci. 13, 1–9 (2019). · DOI
  10. Sprung, C. L. et al. End-of-life practices in european intensive care units: the ethicus study. JAMA 290, 790–797 (2003). · DOI
  11. Owen, A. M. et al. Detecting awareness in the vegetative state. Sci. AAAS 313, 1402 (2006).
  12. Monti, M. M. et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362, 579–589 (2010). · DOI
  13. Cruse, D. et al. Bedside detection of awareness in the vegetative state: a cohort study. Lancet 378, 2088–2094 (2011). · DOI
  14. Vaughan, T. M. et al. The Wadsworth BCI Research And Development Program: At Home With BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 229–233 (2006). · DOI
  15. Coyle, D., Stow, J., McCreadie, K., McElligott, J. & Carroll, Á. Sensorimotor modulation assessment and brain-computer interface training in disorders of consciousness. Arch. Phys. Med. Rehabil. 96, S62–S70 (2015). · DOI
  16. Gibson, R. M., Owen, A. M. & Cruse, D. Brain–computer interfaces for patients with disorders of consciousness. Prog. Brain Res. 228, 241–291 (2016). · DOI
  17. Xiao, J. et al. An auditory BCI system for assisting CRS-R behavioral assessment in patients with disorders of consciousness. Sci. Rep. 6, 1–13 (2016). · DOI
  18. Wang, F. et al. Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness. J. Neural Eng. 14, 1–9 (2017). · DOI
  19. Royal College of Physicians. Prolonged Disorders of Consciousness Following Sudden Onset Brain Injury: National Clinical Guidelines https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Royal+College+of+Physicians.+Prolonged+disorders+of+consciousness+following+sudden+onset+brain+injury%3A+National+clinical+guidelines&btnG= (2020).
  20. Magnani, F. G., Barbadoro, F., Cacciatore, M. & Leonardi, M. The importance of instrumental assessment in disorders of consciousness: a comparison between American, European, and UK International recommendations. Crit. Care 26, 1–9 (2022). · DOI
  21. Giacino, J. T. et al. Practice guideline update recommendations summary: Disorders of consciousness. Neurology 91, 450–460 (2018). · DOI
  22. Pan, J. et al. Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface. J. Neural Eng. 11, 056007 (2014). · DOI
  23. Li, Y. et al. Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system. BMC Neurol. 15, 1–14 (2015). · DOI
  24. Annen, J. et al. Auditory and somatosensory P3 are complementary for the assessment of patients with disorders of consciousness. Brain Sci. 10, 1–14 (2020). · DOI
  25. Huang, J. et al. Hybrid asynchronous brain-computer interface for yes/no communication in patients with disorders of consciousness. J. Neural Eng. 18, 056001 (2021). · DOI
  26. Li, Y. et al. Multimodal BCIs: target detection, multidimensional control, and awareness evaluation in patients with disorder of consciousness. Proc. IEEE 104, 332–352 (2016). · DOI · Paper page
  27. Han, C. H. et al. Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in patient. J. Neuroeng. Rehabil. 16, 1–13 (2019). · DOI
  28. Formaggio, E. et al. EEG to identify attempted movement in Unresponsive Wakefulness Syndrome. Clin. EEG Neurosci. 51, 339–347 (2020). · DOI
  29. Kim, N. et al. Cognitive-motor dissociation following pediatric brain injury: What about the children? Neurol. Clin. Pract. 12, 248–257 (2022). · DOI
  30. Demertzi, A. et al. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain 138, 2619–2631 (2015). · DOI
  31. Threlkeld, Z. D. et al. Functional networks reemerge during recovery of consciousness after acute severe traumatic brain injury. Cortex 106, 299–308 (2018). · DOI
  32. Carrière, M. et al. Auditory localization should be considered as a sign of minimally conscious state based on multimodal findings. Brain Commun. 2, 1–15 (2020). · DOI
  33. Shah, S. A. et al. Executive attention deficits after traumatic brain injury reflect impaired recruitment of resources. NeuroImage Clin. 14, 233–241 (2017). · DOI
  34. Li, H. et al. Functional networks in prolonged disorders of consciousness. Front. Neurosci. 17, 1113695 (2023). · DOI
  35. Cacciola, A. et al. Functional brain network topology discriminates between patients with minimally conscious state and Unresponsive Wakefulness Syndrome. J. Clin. Med. 8, 306 (2019). · DOI
  36. Aubinet, C. et al. Clinical subcategorization of minimally conscious state according to resting functional connectivity. Hum. Brain Mapp. 39, 4519–4532 (2018). · DOI
  37. Chen, H. et al. Disturbed functional connectivity and topological properties of the frontal lobe in minimally conscious state based on resting-state fNIRS. Front. Neurosci. 17, 1118395 (2023). · DOI
  38. Enciso-Olivera, C. O. et al. Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness. Sci. Rep. 11, 1–12 (2021). · DOI
  39. Edlow, B. L., Claassen, J., Schiff, N. D. & Greer, D. M. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat. Rev. Neurol. 17, 135–156 (2021). · DOI
  40. Dayan, N. et al. Towards answering questions in disorders of consciousness and Locked-In Syndrome with a SMR-BCI. In Proc. 8th Graz Brain-Computer Interface Conference https://doi.org/10.3217/978-3-85125-682-6-65 (2019). · DOI
  41. Coyle, D. et al. Towards electroencephalography-based consciousness assessment and cognitive function profiling in prolonged disorders of consciousness. Research Square, https://doi.org/10.21203/rs.3.rs-2349135/v1 (2022). · DOI
  42. du Bois, A. N. et al. An evaluation of combined objective neurophysiologic markers to aid assessment of prolonged disorders of consciousness (PDoC). Preprint at medRxiv https://doi.org/10.1101/2024.10.09.24315104 (2024). · DOI
  43. Perrin, F., Castro, M., Tillmann, B. & Luauté, J. Promoting the use of personally relevant stimuli for investigating patients with disorders of consciousness. Front. Psychol. 6, 1–9 (2015). · DOI
  44. Kempny, A. M. et al. Patients with a severe prolonged Disorder of Consciousness can show classical EEG responses to their own name compared with others’ names. NeuroImage Clin. 19, 311–319 (2018). · DOI
  45. De Guise, E. et al. The Montreal Cognitive Assessment in persons with traumatic brain injury. Appl. Neuropsychol. 21, 128–135 (2014).
  46. Turner-Stokes, L. F., Wade, D. & Playford, D. Prolonged Disorders of Consciousness: National Clinical Guidelines. (Royal College of Physicians, London, 2013). · DOI
  47. Giacino, J. T., Kalmar, K. & Whyte, J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch. Phys. Med. Rehabil. 85, 2020–2029 (2004). · DOI
  48. Bodien, Y. G., Carlowicz, C. A., Chatelle, C. & Giacino, J. T. Sensitivity and specificity of the coma recovery scale-revised total score in detection of conscious awareness. Arch. Phys. Med. Rehabil. 97, 490–492 (2016). · DOI
  49. Shiel, A. et al. The Wessex Head Injury Matrix (WHIM) Main Scale: a preliminary report on a scale to assess and monitor patient recovery after severe head injury. Clin. Rehabil. 14, 408–416 (2000). · DOI
  50. Blume, C., del Giudice, R., Wislowska, M., Lechinger, J. & Schabus, M. Across the consciousness continuum—from unresponsive wakefulness to sleep. Front. Hum. Neurosci. 9, 1–14 (2015). · DOI
  51. Ang, K. K., Chin, Z. Y., Zhang, H. & Guan, C. Filter Bank Common Spatial Pattern (FBCSP) in brain-computer interface. In Proc. International Joint Conference on Neural Networks 2390–2397 https://doi.org/10.1109/IJCNN.2008.4634130 (2008). · DOI · Paper page
  52. Pohjalainen, J., Räsänen, O. & Kadioglu, S. Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput. Speech Lang. 29, 145–171 (2015). · DOI
  53. Korik, A. et al. Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain–computer interface training of a cybathlete who has tetraplegia. J. Neuroeng. Rehabil. https://doi.org/10.1186/s12984-022-01073-9 (2022). · DOI
  54. Ang, K. K., Chin, Z. Y., Wang, C., Guan, C. & Zhang, H. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front. Neurosci. 6, 1–9 (2012). · DOI · Paper page
  55. Korik, A., Sosnik, R., Siddique, N. & Coyle, D. Decoding imagined 3D arm movement trajectories from EEG to control two virtual arms—a pilot study. Front. Neurorobot. 13, 1–22 (2019). · DOI
  56. Lotte, F. & Guan, C. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58, 355–362 (2011). · DOI
  57. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F. & Arnaldi, B. A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4, R1–R13 (2007). · DOI · Paper page
  58. Coyle, D., Dayan, N., Stow, J., McElligott, J. & Carroll, A. Answering questions in Prolonged disorders of consciousness with a brain-computer interface. In Proceedings of the 7th International BCI Meeting (BCI Society, 2018).
  59. Mueller-Putz, G., Scherer, R., Brunner, C., Leeb, R. & Pfurtscheller, G. Better than random: a closer look on BCI results. Int. J. Bioelectromagn. 10, 52–55 (2008).
  60. Ojala, M. & Garriga, G. C. Permutation tests for studying classifier performance. J. Mach. Learn. Res. 11, 1833–1863 (2010).
  61. Pfurtscheller, G. et al. Current trends in Graz Brain-Computer Interface (BCI) research. IEEE Trans. Rehabil. Eng. 8, 216–219 (2000). · DOI
  62. McCreadie, K. A., Coyle, D. H. & Prasad, G. Sensorimotor learning with stereo auditory feedback for a brain-computer interface. Med. Biol. Eng. Comput. 51, 285–293 (2013). · DOI
  63. Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014). · DOI
  64. Karrasch, M., Laine, M., Rapinoja, P. & Krause, C. M. Effects of normal aging on event-related desynchronization/synchronization during a memory task in humans. Neurosci. Lett. 366, 18–23 (2004). · DOI
  65. Stawarczyk, D., Majerus, S., Maquet, P. & D’Argembeau, A. Neural correlates of ongoing conscious experience: Both task-unrelatedness and stimulus-independence are related to default network activity. PLoS ONE 6, e16997 (2011). · DOI · Paper page
  66. Wu, W. et al. Is frontoparietal electroencephalogram activity related to the level of functional disability in patients emerging from a minimally conscious state? A preliminary study. Front. Hum. Neurosci. 16, 1–10 (2022). · DOI
  67. Qu, S. et al. Analyzing brain-activation responses to auditory stimuli improves the diagnosis of a disorder of consciousness by non-linear dynamic analysis of the EEG. Sci. Rep. 14, 1–13 (2024). · DOI
  68. Zheng, Z. S., Reggente, N., Lutkenhoff, E., Owen, A. M. & Monti, M. M. Disentangling disorders of consciousness: insights from diffusion tensor imaging and machine learning. Hum. Brain Mapp. 38, 431–443 (2017). · DOI
  69. Di Perri, C. et al. Neural correlates of consciousness in patients who have emerged from a minimally conscious state: a cross-sectional multimodal imaging study. Lancet Neurol. 15, 830–842 (2016). · DOI
  70. Haufe, S. & Ewald, A. A simulation framework for benchmarking EEG-based brain connectivity estimation methodologies. Brain Topogr. 32, 625–642 (2019). · DOI
  71. Pascual-Marqui, R. D. et al. Assessing interactions in the brain with exact low resolution electromagnetic tomography. Zur. Open Repos. Arch. 369, 1–22 (2011).
  72. Dattola, S. et al. Effect of sensor density on eLORETA source localization accuracy. in Smart Innovation, Systems and Technologies - Neural Approaches to Dynamics of Signal Exchanges Vol. 151 (eds Esposito, A., Faundez-Zanuy, M., Morabito, F. C. & Pasero, E.) 403–414 (Springer, 2020). · DOI
  73. Akalin Acar, Z. & Makeig, S. Effects of forward model errors on EEG source localization. Brain Topogr. 26, 378–396 (2013). · DOI
  74. Brodbeck, V. et al. Electroencephalographic source imaging: A prospective study of 152 operated epileptic patients. Brain 134, 2887–2897 (2011). · DOI · Paper page
  75. Michel, C. M. & Brunet, D. EEG source imaging: a practical review of the analysis steps. Front. Neurol. 10, 325 (2019). · DOI
  76. Pascual-Marqui, R. D. Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. Preprint at arXiv http://arxiv.org/abs/0710.3341 (2007). · arXiv
  77. Sanchez-Bornot, J. M. et al. High-dimensional brain-wide functional connectivity mapping in magnetoencephalography. J. Neurosci. Methods 348, 108991 (2021). · DOI
  78. Li, R. et al. The perils and pitfalls of block design for EEG classification experiments. IEEE Trans. Pattern Anal. Mach. Intell. 43, 316–333 (2021).
  79. Ahn, M., Cho, H., Ahn, S. & Jun, S. C. High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery. PLoS ONE 8, e80886 (2013). · DOI
  80. Ahn, M. & Jun, S. C. Performance variation in motor imagery brain-computer interface: a brief review. J. Neurosci. Methods 243, 103–110 (2015). · DOI
  81. Di, H., Boly, M., Weng, X., Ledoux, D. & Laureys, S. Neuroimaging activation studies in the vegetative state: Predictors of recovery? Clin. Med. 8, 502–507 (2008). · DOI
  82. Giacino, J. T., Fins, J. J., Laureys, S. & Schiff, N. D. Disorders of consciousness after acquired brain injury: the state of the science. Nat. Rev. Neurol. 10, 99–114 (2014). · DOI
  83. Schiff, N. D. et al. Brain–computer interfaces for communication in patients with disorders of consciousness: a gap analysis and scientific roadmap. Neurocrit. Care 41, 129–145 (2024). · DOI
  84. Combrisson, E. & Jerbi, K. Exceeding chance level by chance: the caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods 250, 126–136 (2015). · DOI
  85. Laureys, S., Owen, A. M. & Schiff, N. D. Brain function in coma, vegetative state, and related disorders. Lancet Neurol 3, 537–546 (2004). · DOI · Paper page
  86. Chennu, S. et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain 140, 2120–2132 (2017). · DOI · Paper page
  87. Wang, Q., Huang, Y., Meng, X., Feng, Z. & Bai, Y. Neuroimaging and electrophysiology techniques unveiling the mystery of disorders of consciousness: a narrative review. 86–104. https://doi.org/10.4103/ATN.ATN-D-24-00006 (2024). · DOI
  88. Ballanti, S. et al. EEG-based methods for recovery prognosis of patients with disorders of consciousness: a systematic review. Clin. Neurophysiol. 144, 98–114 (2022). · DOI
  89. Myrden, A. & Chau, T. Effects of user mental state on EEG-BCI performance. Front. Hum. Neurosci. 9, 1–11 (2015). · DOI
  90. Sugiyama, M., Krauledat, M. & Muller, K.-R. Covariate shift adaptation by importance weighted cross validation. J. Mach. Learn. Res. 8, 985–1005 (2007).
  91. Mohammadi, R., Mahloojifar, A. & Coyle, D. Unsupervised short-term covariate shift minimization for self-paced BCI. In IEEE Sympsosium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain 101–106 (IEEE, 2013). · DOI
  92. Satti, A., Guan, C., Coyle, D. & Prasad, G. A covariate shift minimisation method to alleviate non-stationarity effects for an adaptive brain-computer interface. In Proc. 2010 20th International Conference on Pattern Recognition 105–108. https://doi.org/10.1109/ICPR.2010.34 (IEEE, 2010). · DOI
  93. Arvaneh, M., Guan, C., Ang, K. K. & Quek, C. EEG data space adaptation to reduce inter-session non-stationarity in brain–computer interface. Neural Comput 25, 2146–2171 (2013). · DOI
  94. Samek, W., Member, S. & Meinecke, F. C. Transferring subspaces between subjects in brain-computer interfacing. IEEE Trans. Biomed. Eng. 60, 2289–2298 (2013). · DOI
  95. Spadone, S., Wyczesany, M., Della Penna, S., Corbetta, M. & Capotosto, P. Directed flow of beta band communication during reorienting of attention within the Dorsal Attention Network. Brain Connect. 11, 717–724 (2021). · DOI
  96. Adamczyk, A. K. & Wyczesany, M. Theta-band connectivity within cognitive control brain networks suggests common neural mechanisms for cognitive and implicit emotional control. J. Cogn. Neurosci. 35, 1656–1669 (2023). · DOI
  97. Nolte, G. et al. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol. 115, 2292–2307 (2004). · DOI · Paper page
  98. Bastos, A. M. & Schoffelen, J. M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front. Syst. Neurosci. 9, 1–23 (2016). · DOI
  99. Coyle, D., du Bois, N. & Korik, A. Motor-Imagery Brain-Computer Interface Electroencephalography and Behavioural Assessment Datasets in Prolonged Disorders of Consciousness https://doi.org/10.15125/BATH-01632. · DOI

Metadata

Authors
du Bois, N., Korik, A., Hodge, S., Hudson, L., Elahi, A. S., Bigirimana, A., Dayan, N., Sanchez-Bornot, J. M., McCann, A., Yelden, K., Bradley, L., Nair, K. P. S., Judge, S., Hoad, D., Vines, E., Harilal, V., Parke, S., Johnson, P., Pogue, J., Dodds, E., Salawu, A., Carson, R., McCreadie, K., Stow, J., McElligott, J., Carroll, A., & Coyle, D.
Venue
Communications Medicine
Publisher
Springer Science and Business Media LLC
Published
2026-04-17
Volume
6
Issue
1
ISSN
2730-664X
DOI
10.1038/s43856-026-01574-x
Cited by
2
References
5
Language
en
Licence
Funders
RCUK | Engineering and Physical Sciences Research Council (EP/T022175, EP/V025724/1)

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