Skip to main content

Advertisement

SpringerLink
Log in

Brain–Computer Interfaces for Communication in Patients with Disorders of Consciousness: A Gap Analysis and Scientific Roadmap

  • Original work
  • Published:
Neurocritical Care Aims and scope Submit manuscript

Abstract

Background

We developed a gap analysis that examines the role of brain–computer interfaces (BCI) in patients with disorders of consciousness (DoC), focusing on their assessment, establishment of communication, and engagement with their environment.

Methods

The Curing Coma Campaign convened a Coma Science work group that included 16 clinicians and neuroscientists with expertise in DoC. The work group met online biweekly and performed a gap analysis of the primary question.

Results

We outline a roadmap for assessing BCI readiness in patients with DoC and for advancing the use of BCI devices in patients with DoC. Additionally, we discuss preliminary studies that inform development of BCI solutions for communication and assessment of readiness for use of BCIs in DoC study participants. Special emphasis is placed on the challenges posed by the complex pathophysiologies caused by heterogeneous brain injuries and their impact on neuronal signaling. The differences between one-way and two-way communication are specifically considered. Possible implanted and noninvasive BCI solutions for acute and chronic DoC in adult and pediatric populations are also addressed.

Conclusions

We identify clinical and technical gaps hindering the use of BCI in patients with DoC in each of these contexts and provide a roadmap for research aimed at improving communication for adults and children with DoC, spanning the clinical spectrum from intensive care unit to chronic care.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Claassen J, Doyle K, Matory A, et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med. 2019;380(26):2497–505. https://doi.org/10.1056/NEJMoa181275710.1056/NEJMoa1812757.

    Article  PubMed  Google Scholar 

  2. Edlow BL, Chatelle C, Spencer CA, et al. Early detection of consciousness in patients with acute severe traumatic brain injury. Brain. 2017;140(9):2399–414. https://doi.org/10.1093/brain/awx17610.1093/brain/awx176.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Giacino JT, Katz DI, Schiff ND, et al. Practice guideline update recommendations summary: Disorders of consciousness: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Neurology. 2018;91(10):450–60. https://doi.org/10.1212/WNL.000000000000592610.1212/WNL.0000000000005926.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Edlow BL, Claassen J, Schiff ND, Greer DM. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat Rev Neurol. 2021;17(3):135–56. https://doi.org/10.1038/s41582-020-00428-x10.1038/s41582-020-00428-x.

    Article  PubMed  Google Scholar 

  5. Schiff ND. Cognitive motor dissociation following severe brain injuries. JAMA Neurol. 2015;72(12):1413–5. https://doi.org/10.1001/jamaneurol.2015.289910.1001/jamaneurol.2015.2899.

    Article  PubMed  Google Scholar 

  6. Claassen J, Kondziella D, Alkhachroum A et al. Cognitive Motor dissociation: gap analysis and future directions. Neurocrit Care 2023. https://doi.org/10.1007/s12028-023-01769-3https://doi.org/10.1007/s12028-023-01769-3.

  7. Bodien Y, Allanson J, Cardone P, et al. 14th world congress on brain injury abstracts. Brain Inj. 2023;37(sup1):1–278. https://doi.org/10.1080/02699052.2023.224782210(1080/02699052),pp.2247822,2023.

    Article  Google Scholar 

  8. Monti MM, Vanhaudenhuyse A, Coleman MR, et al. Willful modulation of brain activity in disorders of consciousness. N Engl J Med. 2010;362(7):579–89. https://doi.org/10.1056/NEJMoa090537010.1056/NEJMoa0905370.

    Article  PubMed  CAS  Google Scholar 

  9. Thengone DJ, Voss HU, Fridman EA, Schiff ND. Local changes in network structure contribute to late communication recovery after severe brain injury. Sci Transl Med 2016;8(368):368re5. https://doi.org/10.1126/scitranslmed.aaf6113https://doi.org/10.1126/scitranslmed.aaf6113.

  10. Fins J. Rights come to mind: brain injury, ethics, and the struggle for consciousness. Cambridge University Press;2015.

  11. Wolpaw JR, Millán JDR, Ramsey NF. Brain-computer interfaces: definitions and principles. Handb Clin Neurol. 2020;168:15–23. https://doi.org/10.1016/B978-0-444-63934-9.00002-010.1016/B978-0-444-63934-9.00002-0.

    Article  PubMed  Google Scholar 

  12. Saha S, Mamun KA, Ahmed K, et al. Progress in brain computer interface: challenges and opportunities. Front Syst Neurosci. 2021;15: 578875. https://doi.org/10.3389/fnsys.2021.57887510.3389/fnsys.2021.578875.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Pandarinath C, Nuyujukian P, Blabe CH, et al. High performance communication by people with paralysis using an intracortical brain-computer interface. Elife. 2017;6: e18554. https://doi.org/10.7554/eLife.1855410.7554/eLife.18554.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Brandman DM, Cash SS, Hochberg LR. Review: human intracortical recording and neural decoding for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2017;25(10):1687–96. https://doi.org/10.1109/TNSRE.2017.267744310.1109/TNSRE.2017.2677443.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Churchland MM, Cunningham JP, Kaufman MT, et al. Neural population dynamics during reaching. Nature. 2012;487(7405):51–6. https://doi.org/10.1038/nature1112910.1038/nature11129.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27(3):379–423.

    Article  Google Scholar 

  17. Fried-Oken M, Mooney A, Peters B, Oken B. A clinical screening protocol for the RSVP Keyboard brain-computer interface. Disabil Rehabil Assist Technol. 2015;10(1):11–8. https://doi.org/10.3109/17483107.2013.83668410(3109/17483107),pp.836684,2013.

    Article  PubMed  Google Scholar 

  18. Smart CM, Giacino JT, Cullen T, et al. A case of locked-in syndrome complicated by central deafness. Nat Clin Pract Neurol. 2008;4(8):448–53. https://doi.org/10.1038/ncpneuro082310.1038/ncpneuro0823.

    Article  PubMed  Google Scholar 

  19. Bensch M, Martens S, Halder S, et al. Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography. J Neural Eng. 2014;11(2): 026006. https://doi.org/10.1088/1741-2560/11/2/02600610.1088/1741-2560/11/2/026006.

    Article  PubMed  Google Scholar 

  20. Fernández-Espejo D, Rossit S, Owen AM. A Thalamocortical mechanism for the absence of overt motor behavior in covertly aware patients. JAMA Neurol. 2015;72(12):1442–50. https://doi.org/10.1001/jamaneurol.2015.261410.1001/jamaneurol.2015.2614.

    Article  PubMed  Google Scholar 

  21. Vansteensel MJ, Jarosiewicz B. Brain–computer interfaces for communication. Handb Clin Neurol. 2020;168:67–85. https://doi.org/10.1016/B978-0-444-63934-9.00007-X10.1016/B978-0-444-63934-9.00007-X.

    Article  PubMed  Google Scholar 

  22. Laureys S, Schiff ND. Coma and consciousness: paradigms (re)framed by neuroimaging. Neuroimage. 2012;61(2):478–91. https://doi.org/10.1016/j.neuroimage.2011.12.04110.1016/j.neuroimage.2011.12.041.

    Article  PubMed  Google Scholar 

  23. Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Detecting awareness in the vegetative state. Science. 2006;313(5792):1402. https://doi.org/10.1126/science.113019710.1126/science.1130197.

    Article  PubMed  CAS  Google Scholar 

  24. Comanducci A, Boly M, Claassen J, et al. Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group. Clin Neurophysiol. 2020;131(11):2736–65. https://doi.org/10.1016/j.clinph.2020.07.01510.1016/j.clinph.2020.07.015.

    Article  PubMed  CAS  Google Scholar 

  25. Bardin JC, Fins JJ, Katz DI, et al. Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain. 2011;134(3):769–82. https://doi.org/10.1093/brain/awr00510.1093/brain/awr005.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Cruse D, Chennu S, Chatelle C, et al. Bedside detection of awareness in the vegetative state: a cohort study. Lancet. 2011;378(9809):2088–94. https://doi.org/10.1016/S0140-6736(11)61224-510.1016/S0140-6736(11)61224-5.

    Article  PubMed  Google Scholar 

  27. Goldfine AM, Victor JD, Conte MM, Bardin JC, Schiff ND. Determination of awareness in patients with severe brain injury using EEG power spectral analysis. Clin Neurophysiol. 2011;122(11):2157–68. https://doi.org/10.1016/j.clinph.2011.03.02210.1016/j.clinph.2011.03.022.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Gibson RM, Fernández-Espejo D, Gonzalez-Lara LE, et al. Multiple tasks and neuroimaging modalities increase the likelihood of detecting covert awareness in patients with disorders of consciousness. Front Hum Neurosci. 2014;8:950. https://doi.org/10.3389/fnhum.2014.0095010.3389/fnhum.2014.00950.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Laureys S, Faymonville ME, Peigneux P, et al. Cortical processing of noxious somatosensory stimuli in the persistent vegetative state. Neuroimage. 2002;17(2):732–41.

    Article  PubMed  CAS  Google Scholar 

  30. Boly M, Faymonville ME, Schnakers C, et al. Perception of pain in the minimally conscious state with PET activation: an observational study. Lancet Neurol. 2008;7(11):1013–20. https://doi.org/10.1016/S1474-4422(08)70219-910.1016/S1474-4422(08)70219-9.

    Article  PubMed  Google Scholar 

  31. Schiff ND, Rodriguez-Moreno D, Kamal A, et al. fMRI reveals large-scale network activation in minimally conscious patients. Neurology. 2005;64(3):514–23. https://doi.org/10.1212/01.WNL.0000150883.10285.4410.1212/01.WNL.0000150883.10285.44.

    Article  PubMed  CAS  Google Scholar 

  32. Menon DK, Owen AM, Williams EJ, et al. Cortical processing in persistent vegetative state. Wolfson Brain Imaging Centre Team Lancet. 1998;352(9123):200. https://doi.org/10.1016/s0140-6736(05)77805-310.1016/s0140-6736(05)77805-3.

    Article  PubMed  CAS  Google Scholar 

  33. Monti MM, Pickard JD, Owen AM. Visual cognition in disorders of consciousness: from V1 to top-down attention. Hum Brain Mapp. 2013;34(6):1245–53. https://doi.org/10.1002/hbm.2150710.1002/hbm.21507.

    Article  PubMed  Google Scholar 

  34. Bekinschtein TA, Dehaene S, Rohaut B, Tadel F, Cohen L, Naccache L. Neural signature of the conscious processing of auditory regularities. Proc Natl Acad Sci USA. 2009;106(5):1672–7. https://doi.org/10.1073/pnas.080966710610.1073/pnas.0809667106.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Sattin D, Bruzzone MG, Ferraro S, et al. Olfactory discrimination in disorders of consciousness: a new sniff protocol. Brain Behav. 2019;9(8): e01273. https://doi.org/10.1002/brb3.127310.1002/brb3.1273.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Arzi A, Rozenkrantz L, Gorodisky L, et al. Olfactory sniffing signals consciousness in unresponsive patients with brain injuries. Nature. 2020;581(7809):428–33. https://doi.org/10.1038/s41586-020-2245-510.1038/s41586-020-2245-5.

    Article  PubMed  CAS  Google Scholar 

  37. Wijnen VJ, van Boxtel GJ, Eilander HJ, de Gelder B. Mismatch negativity predicts recovery from the vegetative state. Clin Neurophysiol. 2007;118(3):597–605. https://doi.org/10.1016/j.clinph.2006.11.02010.1016/j.clinph.2006.11.020.

    Article  PubMed  CAS  Google Scholar 

  38. Faugeras F, Rohaut B, Weiss N, et al. Event related potentials elicited by violations of auditory regularities in patients with impaired consciousness. Neuropsychologia. 2012;50(3):403–18. https://doi.org/10.1016/j.neuropsychologia.2011.12.01510.1016/j.neuropsychologia.2011.12.015.

    Article  PubMed  Google Scholar 

  39. Kotchoubey B, Lang S, Mezger G, et al. Information processing in severe disorders of consciousness: vegetative state and minimally conscious state. Clin Neurophysiol. 2005;116(10):2441–53. https://doi.org/10.1016/j.clinph.2005.03.02810.1016/j.clinph.2005.03.028.

    Article  PubMed  CAS  Google Scholar 

  40. Rohaut B, Faugeras F, Chausson N, et al. Probing ERP correlates of verbal semantic processing in patients with impaired consciousness. Neuropsychologia. 2015;66:279–92. https://doi.org/10.1016/j.neuropsychologia.2014.10.01410.1016/j.neuropsychologia.2014.10.014.

    Article  PubMed  Google Scholar 

  41. Perrin F, Schnakers C, Schabus M, et al. Brain response to one’s own name in vegetative state, minimally conscious state, and locked-in syndrome. Arch Neurol. 2006;63(4):562–9. https://doi.org/10.1001/archneur.63.4.56210.1001/archneur.63.4.562.

    Article  PubMed  Google Scholar 

  42. Di HB, Yu SM, Weng XC, et al. Cerebral response to patient’s own name in the vegetative and minimally conscious states. Neurology. 2007;68(12):895–9. https://doi.org/10.1212/01.wnl.0000258544.79024.d010.1212/01.wnl.0000258544.79024.d0.

    Article  PubMed  CAS  Google Scholar 

  43. Lulé D, Noirhomme Q, Kleih SC, et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clin Neurophysiol. 2013;124(1):101–6. https://doi.org/10.1016/j.clinph.2012.04.03010.1016/j.clinph.2012.04.030.

    Article  PubMed  Google Scholar 

  44. Schettini F, Risetti M, Arico P et al. P300 latency Jitter occurrence in patients with disorders of consciousness: toward a better design for Brain Computer Interface applications. Annu Int Conf IEEE Eng Med Biol Soc 2015;6178–81. https://doi.org/10.1109/EMBC.2015.7319803

  45. Li Y, Pan J, He Y, et al. Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system. BMC Neurol. 2015;15:259. https://doi.org/10.1186/s12883-015-0521-z10.1186/s12883-015-0521-z.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Wang F, He Y, Pan J, et al. A novel audiovisual brain-computer interface and its application in awareness detection. Sci Rep. 2015;5:9962. https://doi.org/10.1038/srep0996210.1038/srep09962.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Guger C, Spataro R, Pellas F, et al. Assessing command-following and communication with vibro-tactile P300 brain–computer interface tools in patients with unresponsive wakefulness syndrome. Front Neurosci. 2018;12:423. https://doi.org/10.3389/fnins.2018.0042310.3389/fnins.2018.00423.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Annen J, Laureys S, Gosseries O. Brain–computer interfaces for consciousness assessment and communication in severely brain-injured patients. Handb Clin Neurol. 2020;168:137–52. https://doi.org/10.1016/B978-0-444-63934-9.00011-110.1016/B978-0-444-63934-9.00011-1.

    Article  PubMed  Google Scholar 

  49. Wolpaw JR, Birbaumer N, Heetderks WJ, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng. 2000;8(2):164–73. https://doi.org/10.1109/tre.2000.84780710.1109/tre.2000.847807.

    Article  PubMed  CAS  Google Scholar 

  50. Casali AG, Gosseries O, Rosanova M, et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci Transl Med 2013;5(198):198ra105. https://doi.org/10.1126/scitranslmed.3006294

  51. Fischer C, Luaute J, Morlet D. Event-related potentials (MMN and novelty P3) in permanent vegetative or minimally conscious states. Clin Neurophysiol. 2010;121(7):1032–42. https://doi.org/10.1016/j.clinph.2010.02.00510.1016/j.clinph.2010.02.005.

    Article  PubMed  Google Scholar 

  52. Mofakham S, Fry A, Adachi J, et al. Electrocorticography reveals thalamic control of cortical dynamics following traumatic brain injury. Commun Biol. 2021;4(1):1210. https://doi.org/10.1038/s42003-021-02738-210.1038/s42003-021-02738-2.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Mofakham S, Liu Y, Hensley A, et al. Injury to thalamocortical projections following traumatic brain injury results in attractor dynamics for cortical networks. Prog Neurobiol. 2022;210: 102215. https://doi.org/10.1016/j.pneurobio.2022.10221510.1016/j.pneurobio.2022.102215.

    Article  PubMed  Google Scholar 

  54. Wagner FB, Mignardot JB, Le Goff-Mignardot CG, et al. Targeted neurotechnology restores walking in humans with spinal cord injury. Nature. 2018;563(7729):65–71. https://doi.org/10.1038/s41586-018-0649-210.1038/s41586-018-0649-2.

    Article  PubMed  CAS  Google Scholar 

  55. Rowald A, Komi S, Demesmaeker R, et al. Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis. Nat Med. 2022;28(2):260–71. https://doi.org/10.1038/s41591-021-01663-510.1038/s41591-021-01663-5.

    Article  PubMed  CAS  Google Scholar 

  56. Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spinal cord injury using a brain-spine interface. Nature. 2023;618(7963):126–33. https://doi.org/10.1038/s41586-023-06094-510.1038/s41586-023-06094-5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Scangos KW, Khambhati AN, Daly PM, et al. Closed-loop neuromodulation in an individual with treatment-resistant depression. Nat Med. 2021;27(10):1696–700. https://doi.org/10.1038/s41591-021-01480-w10.1038/s41591-021-01480-w.

    Article  PubMed  CAS  Google Scholar 

  58. Shirvalkar P, Veuthey TL, Dawes HE, Chang EF. Closed-loop deep brain stimulation for refractory chronic pain. Front Comput Neurosci. 2018;12:18. https://doi.org/10.3389/fncom.2018.0001810.3389/fncom.2018.00018.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Shirvalkar P, Prosky J, Chin G, et al. First-in-human prediction of chronic pain state using intracranial neural biomarkers. Nat Neurosci. 2023;26(6):1090–9. https://doi.org/10.1038/s41593-023-01338-z10.1038/s41593-023-01338-z.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Krack P, Volkmann J, Tinkhauser G, Deuschl G. Deep brain stimulation in movement disorders: from experimental surgery to evidence-based therapy. Mov Disord. 2019;34(12):1795–810. https://doi.org/10.1002/mds.2786010.1002/mds.27860.

    Article  PubMed  Google Scholar 

  61. Saalmann YB, Mofakham S, Mikell CB, Djuric PM. Microscale multicircuit brain stimulation: achieving real-time brain state control for novel applications. Curr Res Neurobiol. 2023;4: 100071. https://doi.org/10.1016/j.crneur.2022.10007110.1016/j.crneur.2022.100071.

    Article  PubMed  CAS  Google Scholar 

  62. Rangayyan RM. Biomedical signal analysis. Wiley;2015.

  63. Kamble A, Ghare P, Kumar V. Machine-learning-enabled adaptive signal decomposition for a brain-computer interface using EEG. Biomed Signal Process Control. 2022;74: 103526.

    Article  Google Scholar 

  64. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016;374(2065):20150202. https://doi.org/10.1098/rsta.2015.020210.1098/rsta.2015.0202.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Bell AJ, Sejnowski TJ. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995;7(6):1129–59. https://doi.org/10.1162/neco.1995.7.6.112910.1162/neco.1995.7.6.1129.

    Article  PubMed  CAS  Google Scholar 

  66. Lee TW, Girolami M, Sejnowski TJ. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 1999;11(2):417–41. https://doi.org/10.1162/08997669930001671910.1162/089976699300016719.

    Article  PubMed  CAS  Google Scholar 

  67. Hyvärinen A, Karhunen J, Oja E. Independent component analysis. Wiley;2001.

  68. Calhoun VD, Adali T, Pearlson GD, Kiehl KA. Neuronal chronometry of target detection: fusion of hemodynamic and event-related potential data. Neuroimage. 2006;30(2):544–53. https://doi.org/10.1016/j.neuroimage.2005.08.06010.1016/j.neuroimage.2005.08.060.

    Article  PubMed  CAS  Google Scholar 

  69. Moosmann M, Eichele T, Nordby H, Hugdahl K, Calhoun VD. Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation. Int J Psychophysiol. 2008;67(3):212–21. https://doi.org/10.1016/j.ijpsycho.2007.05.01610.1016/j.ijpsycho.2007.05.016.

    Article  PubMed  Google Scholar 

  70. Mosayebi R, Hossein-Zadeh G-A. Correlated coupled matrix tensor factorization method for simultaneous EEG-fMRI data fusion. Biomed Signal Process Control. 2020;62: 102071. https://doi.org/10.1016/j.bspc.2020.10207110.1016/j.bspc.2020.102071.

    Article  Google Scholar 

  71. Van Eyndhoven S, Dupont P, Tousseyn S, et al. Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data. Neuroimage. 2021;228: 117652. https://doi.org/10.1016/j.neuroimage.2020.11765210.1016/j.neuroimage.2020.117652.

    Article  PubMed  Google Scholar 

  72. Aggarwal S, Chugh N. Signal processing techniques for motor imagery brain computer interface: a review. Array. 2019;1–2: 100003. https://doi.org/10.1016/j.array.2019.10000310.1016/j.array.2019.100003.

    Article  Google Scholar 

  73. Molteni E, Arrigoni F, Bardoni A et al. Bedside assessment of residual functional activation in minimally conscious state using NIRS and general linear models. Annu Int Conf IEEE Eng Med Biol Soc 2013:3551–4. https://doi.org/10.1109/EMBC.2013.6610309

  74. Pincherle A, Rossi F, Jöhr J, et al. Early discrimination of cognitive motor dissociation from disorders of consciousness: pitfalls and clues. J Neurol. 2021;268(1):178–88. https://doi.org/10.1007/s00415-020-10125-w10.1007/s00415-020-10125-w.

    Article  PubMed  Google Scholar 

  75. Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004;85(12):2020–9. https://doi.org/10.1016/j.apmr.2004.02.03310.1016/j.apmr.2004.02.033.

    Article  PubMed  Google Scholar 

  76. Pincherle A, Jöhr J, Chatelle C, et al. Motor behavior unmasks residual cognition in disorders of consciousness. Ann Neurol. 2019;85(3):443–7. https://doi.org/10.1002/ana.2541710.1002/ana.25417.

    Article  PubMed  Google Scholar 

  77. Onofrj M, Melchionda D, Thomas A, Fulgente T. Reappearance of event-related P3 potential in locked-in syndrome. Cogn Brain Res. 1996;4(2):95–7. https://doi.org/10.1016/0926-6410(96)00021-310.1016/0926-6410(96)00021-3.

    Article  CAS  Google Scholar 

  78. Forgacs PB, Fridman EA, Goldfine AM, Schiff ND. Isolation syndrome after cardiac arrest and therapeutic hypothermia. Front Neurosci. 2016;10:259. https://doi.org/10.3389/fnins.2016.0025910.3389/fnins.2016.00259.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Chennu S, Finoia P, Kamau E, et al. Spectral signatures of reorganised brain networks in disorders of consciousness. PLoS Comput Biol. 2014;10(10): e1003887. https://doi.org/10.1371/journal.pcbi.100388710.1371/journal.pcbi.1003887.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Sitt JD, King JR, El Karoui I, et al. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. Brain. 2014;137(8):2258–70. https://doi.org/10.1093/brain/awu14110.1093/brain/awu141.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Engemann DA, Raimondo F, King JR, et al. Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain. 2018;141(11):3179–92. https://doi.org/10.1093/brain/awy25110.1093/brain/awy251.

    Article  PubMed  Google Scholar 

  82. Forgacs PB, Conte MM, Fridman EA, Voss HU, Victor JD, Schiff ND. Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following. Ann Neurol. 2014;76(6):869–79. https://doi.org/10.1002/ana.2428310.1002/ana.24283.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Curley WH, Forgacs PB, Voss HU, Conte MM, Schiff ND. Characterization of EEG signals revealing covert cognition in the injured brain. Brain. 2018;141(5):1404–21. https://doi.org/10.1093/brain/awy07010.1093/brain/awy070.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Giacino JT, Fins JJ, Laureys S, Schiff ND. Disorders of consciousness after acquired brain injury: the state of the science. Nat Rev Neurol. 2014;10(2):99–114. https://doi.org/10.1038/nrneurol.2013.27910.1038/nrneurol.2013.279.

    Article  PubMed  Google Scholar 

  85. Cruse D, Chennu S, Fernández-Espejo D, Payne WL, Young GB, Owen AM. Detecting awareness in the vegetative state: electroencephalographic evidence for attempted movements to command. PLoS ONE. 2012;7(11): e49933. https://doi.org/10.1371/journal.pone.004993310.1371/journal.pone.0049933.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Bekinschtein TA, Coleman MR, Niklison J, Pickard JD, Manes FF. Can electromyography objectively detect voluntary movement in disorders of consciousness. J Neurol Neurosurg Psychiatry. 2008;79(7):826–8. https://doi.org/10.1136/jnnp.2007.13273810.1136/jnnp.2007.132738.

    Article  PubMed  CAS  Google Scholar 

  87. Bekinschtein TA, Shalom DE, Forcato C, et al. Classical conditioning in the vegetative and minimally conscious state. Nat Neurosci. 2009;12(10):1343–9. https://doi.org/10.1038/nn.239110.1038/nn.2391.

    Article  PubMed  CAS  Google Scholar 

  88. Chatelle C, Spencer CA, Cash SS, Hochberg LR, Edlow BL. Feasibility of an EEG-based brain-computer interface in the intensive care unit. Clin Neurophysiol. 2018;129(8):1519–25. https://doi.org/10.1016/j.clinph.2018.04.74710.1016/j.clinph.2018.04.747.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Calabrò RS, Pignolo L, Müller-Eising C, Naro A. Pain perception in disorder of consciousness: a scoping review on current knowledge, clinical applications, and future perspective. Brain Sci. 2021;11(5):665. https://doi.org/10.3390/brainsci1105066510.3390/brainsci11050665.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Noel JP, Chatelle C, Perdikis S, et al. Peri-personal space encoding in patients with disorders of consciousness and cognitive-motor dissociation. Neuroimage Clin. 2019;24: 101940. https://doi.org/10.1016/j.nicl.2019.10194010.1016/j.nicl.2019.101940.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Diserens K, Meyer IA, Jöhr J, et al. A focus on subtle signs and motor behavior to unveil awareness in unresponsive brain-impaired patients. Neurology. 2023;100(24):1144–50. https://doi.org/10.1212/wnl.000000000020706710.1212/wnl.0000000000207067.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Franzova E, Shen Q, Doyle K et al. Injury patterns associated with cognitive motor dissociation. Brain 2023 Aug 14;awad197 [Online ahead of print] https://doi.org/10.1093/brain/awad197

  93. Cosgrove ME, Saadon JR, Mikell CB, et al. Thalamo-prefrontal connectivity correlates with early command-following after severe traumatic brain injury. Front Neurol. 2022;13: 826266. https://doi.org/10.3389/fneur.2022.82626610.3389/fneur.2022.826266.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Zelmann R, Paulk AC, Tian F et al. Differential cortical network engagement during states of un/consciousness in humans. Neuron 2023 Aug 29;S0896–6273(23)00618–9 [Online ahead of print] https://doi.org/10.1016/j.neuron.2023.08.007

  95. Galiotta V, Quattrociocchi I, D’Ippolito M, et al. EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review. Front Hum Neurosci. 2022;16:1040816. https://doi.org/10.3389/fnhum.2022.104081610.3389/fnhum.2022.1040816.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Pan J, Xie Q, He Y, et al. Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface. J Neural Eng. 2014;11(5): 056007. https://doi.org/10.1088/1741-2560/11/5/05600710.1088/1741-2560/11/5/056007.

    Article  PubMed  Google Scholar 

  97. Voss HU, Uluğ AM, Dyke JP, et al. Possible axonal regrowth in late recovery from the minimally conscious state. J Clin Investig. 2006;116(7):2005–11. https://doi.org/10.1172/JCI2702110.1172/JCI27021.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Molteni E, Rocca MA, Strazzer S, et al. A diffusion tensor magnetic resonance imaging study of paediatric patients with severe non-traumatic brain injury. Dev Med Child Neurol. 2017;59(2):199–206. https://doi.org/10.1111/dmcn.1333210.1111/dmcn.13332.

    Article  PubMed  Google Scholar 

  99. Avantaggiato P, Molteni E, Formica F, et al. Polysomnographic sleep patterns in children and adolescents in unresponsive wakefulness syndrome. J Head Trauma Rehabil. 2015;30(5):334–46. https://doi.org/10.1097/HTR.000000000000012210.1097/HTR.0000000000000122.

    Article  PubMed  Google Scholar 

  100. Irzan H, Pozzi M, Chikhladze N, et al. Emerging treatments for disorders of consciousness in paediatric age. Brain Sci. 2022;12(2):198. https://doi.org/10.3390/brainsci1202019810.3390/brainsci12020198.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Shewmon DA, Holmes GL, Byrne PA. Consciousness in congenitally decorticate children: developmental vegetative state as self-fulfilling prophecy. Dev Med Child Neurol. 1999;41(6):364–74. https://doi.org/10.1017/s001216229900082110.1017/s0012162299000821.

    Article  PubMed  CAS  Google Scholar 

  102. Nelson CA, Luciana M. Handbook of developmental cognitive neuroscience. MIT Press;2008.

  103. Mikołajewska E, Mikołajewski D. The prospects of brain–computer interface applications in children. Open Med. 2014;9(1):74–9. https://doi.org/10.2478/s11536-013-0249-310.2478/s11536-013-0249-3.

    Article  Google Scholar 

  104. Orlandi S, House SC, Karlsson P, Saab R, Chau T. Brain-computer interfaces for children with complex communication needs and limited mobility: a systematic review. Front Hum Neurosci. 2021;15: 643294. https://doi.org/10.3389/fnhum.2021.64329410.3389/fnhum.2021.643294.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Ehlers J, Valbuena D, Stiller A, Gräser A. Age-specific mechanisms in an SSVEP-based BCI scenario: evidences from spontaneous rhythms and neuronal oscillators. Comput Intell Neurosci 2012:967305. https://doi.org/10.1155/2012/967305

  106. Volosyak I, Gembler F, Stawicki P. Age-related differences in SSVEP-based BCI performance. Neurocomputing. 2017;250:57–64. https://doi.org/10.1016/j.neucom.2016.08.12110.1016/j.neucom.2016.08.121.

    Article  Google Scholar 

  107. Kinney-Lang E, Kelly D, Floreani ED, et al. Advancing brain-computer interface applications for severely disabled children through a multidisciplinary national network: summary of the inaugural pediatric BCI Canada Meeting. Front Hum Neurosci. 2020;14: 593883. https://doi.org/10.3389/fnhum.2020.59388310.3389/fnhum.2020.593883.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Zhang J, Jadavji Z, Zewdie E, Kirton A. Evaluating if children can use simple brain computer interfaces. Front Hum Neurosci. 2019;13:24. https://doi.org/10.3389/fnhum.2019.0002410.3389/fnhum.2019.00024.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Kim N, O’Sullivan J, Olafson E, et al. Cognitive-motor dissociation following pediatric brain injury: what about the children. Neurol Clin Pract. 2022;12(3):248–57. https://doi.org/10.1212/CPJ.000000000000116910.1212/CPJ.0000000000001169.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Kim N, Watson W, Caliendo E, et al. Objective neurophysiologic markers of cognition after pediatric brain injury. Neurol Clin Pract. 2022;12(5):352–64. https://doi.org/10.1212/CPJ.000000000020006610.1212/CPJ.0000000000200066.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Sanchez JC, Gunduz A, Carney PR, Principe JC. Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics. J Neurosci Methods. 2008;167(1):63–81. https://doi.org/10.1016/j.jneumeth.2007.04.01910.1016/j.jneumeth.2007.04.019.

    Article  PubMed  Google Scholar 

  112. Breshears JD, Gaona CM, Roland JL, et al. Decoding motor signals from the pediatric cortex: implications for brain-computer interfaces in children. Pediatrics. 2011;128(1):e160–8. https://doi.org/10.1542/peds.2010-151910.1542/peds.2010-1519.

    Article  PubMed  Google Scholar 

  113. Pistohl T, Schmidt TS, Ball T, Schulze-Bonhage A, Aertsen A, Mehring C. Grasp detection from human ECoG during natural reach-to-grasp movements. PLoS ONE. 2013;8(1): e54658. https://doi.org/10.1371/journal.pone.005465810.1371/journal.pone.0054658.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Pistohl T, Schulze-Bonhage A, Aertsen A, Mehring C, Ball T. Decoding natural grasp types from human ECoG. Neuroimage. 2012;59(1):248–60. https://doi.org/10.1016/j.neuroimage.2011.06.08410.1016/j.neuroimage.2011.06.084.

    Article  PubMed  Google Scholar 

  115. Willett FR, Kunz EM, Fan C, et al. A high-performance speech neuroprosthesis. Nature. 2023;620(7976):1031–6. https://doi.org/10.1038/s41586-023-06377-x10.1038/s41586-023-06377-x.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Rodriguez Moreno D, Schiff ND, Giacino J, Kalmar K, Hirsch J. A network approach to assessing cognition in disorders of consciousness. Neurology. 2010;75(21):1871–8. https://doi.org/10.1212/WNL.0b013e3181feb25910.1212/WNL.0b013e3181feb259.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Parliamentary Office of Science and Technology, 2020. Brain-Computer Interface. POSTNote No. 614. UK Parliament. Available from: https://post.parliament.uk/research-briefings/post-pn-0614.

  118. Multi-Society Task Force on PVS. Medical aspects of the persistent vegetative state (1). N Engl J Med. 1994;330(21):1499–508. https://doi.org/10.1056/NEJM19940526330210710.1056/NEJM199405263302107.

    Article  Google Scholar 

  119. Giacino JT, Ashwal S, Childs N, et al. The minimally conscious state: definition and diagnostic criteria. Neurology. 2002;58(3):349–53. https://doi.org/10.1212/wnl.58.3.34910.1212/wnl.58.3.349.

    Article  PubMed  Google Scholar 

  120. Thibaut A, Bodien YG, Laureys S, Giacino JT. Minimally conscious state “plus”: diagnostic criteria and relation to functional recovery. J Neurol. 2020;267(5):1245–54. https://doi.org/10.1007/s00415-019-09628-y10.1007/s00415-019-09628-y.

    Article  PubMed  Google Scholar 

  121. Posner JB, Saper CB, Schiff ND, Jan Claassen MD. Plum and Posner’s diagnosis and treatment of stupor and coma. Oxford University Press, 2019.

  122. Schnetzer L, McCoy M, Bergmann J, Kunz A, Leis S, Trinka E. Locked-in syndrome revisited. Ther Adv Neurol Disord. 2023;16:17562864231160872. https://doi.org/10.1177/1756286423116087310.1177/17562864231160873.

    Article  PubMed  PubMed Central  Google Scholar 

  123. Formica F, Pozzi M, Avantaggiato P, et al. Disordered consciousness or disordered wakefulness? The importance of prolonged polysomnography for the diagnosis, drug therapy, and rehabilitation of an unresponsive patient with brain injury. J Clin Sleep Med. 2017;13(12):1477–81. https://doi.org/10.5664/jcsm.685410.5664/jcsm.6854.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Edlow BL, Olchanyi M, Freeman HJ et al. Sustaining wakefulness: Brainstem connectivity in human consciousness. bioRxiv 2023 Jul 15 [Preprint] https://doi.org/10.1101/2023.07.13.548265

  125. Spataro R, Heilinger A, Allison B, et al. Preserved somatosensory discrimination predicts consciousness recovery in unresponsive wakefulness syndrome. Clin Neurophysiol. 2018;129(6):1130–6. https://doi.org/10.1016/j.clinph.2018.02.13110.1016/j.clinph.2018.02.131.

    Article  PubMed  Google Scholar 

  126. Pokorny C, Breitwieser C, Müller-Putz GR. The role of transient target stimuli in a steady-state somatosensory evoked potential-based brain-computer interface setup. Front Neurosci. 2016;10:152. https://doi.org/10.3389/fnins.2016.0015210.3389/fnins.2016.00152.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The authors acknowledge the following sources: the NIH Director’s Office (DP2HD101400) and Chen Institute MGH Research Scholar Award (B.L.E.), F.R.S-FNRS (OG), IAM’s contribution to this paper was made during his tenure as a research assistant in Montreal, Canada, funded by the Gianni Biaggi de Blasys Foundation, Lausanne, Switzerland, NJH acknowledges support from NIH (NIBIB) P41EB018783 and from the Stratton VA Medical Center.

Author information

Authors and Affiliations

  1. Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY, USA

    Nicholas D. Schiff

  2. Departments of Neurology and Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA

    Michael Diringer

  3. Neurology and Acute Neurorehabilitation Unit, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Karin Diserens & Ivo A. Meyer

  4. Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Brian L. Edlow

  5. Coma Science Group, GIGA-Consciousness, Centre du Cerveau, University Hospital of Liège, University of Liège & Centre du Cerveau, Liège, Belgium

    Olivia Gosseries

  6. National Center for Adaptive Neurotechnologies, Stratton VA Medical Center, Albany, NY, USA

    N. Jeremy Hill

  7. Electrical & Computer Engineering Department, State University of New York at Albany, Albany, NY, USA

    N. Jeremy Hill

  8. Veterans Affairs Rehabilitation Research & Development Center for Neurorestoration and Neurotechnology, Rehabilitation Research & Development Service, Providence VA Medical Center, Providence, RI, USA

    Leigh R. Hochberg

  9. School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA

    Leigh R. Hochberg

  10. Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Leigh R. Hochberg

  11. Department of Pediatrics, United Arab Emirates University, Al Ain, United Arab Emirates

    Fatima Y. Ismail

  12. Department of Neurology, Adjunct Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Fatima Y. Ismail

  13. Centre for Advanced Research in Sleep Medicine and Integrated Trauma Centre, Integrated University Health and Social Services Centre (CIUSSS) du Nord-de-L’Île-de-Montréal, Montreal, QC, Canada

    Ivo A. Meyer

  14. Department of Neurosurgery, Stony Brook University Hospital, Stony Brook, NY, USA

    Charles B. Mikell & Sima Mofakham

  15. Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA

    Sima Mofakham

  16. School of Biomedical Engineering and Imaging Sciences, and Centre for Medical Engineering, King’s College London, London, UK

    Erika Molteni

  17. Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA

    Leonard Polizzotto

  18. Department of Radiology, Weill Cornell Medical College, New York, NY, USA

    Sudhin A. Shah

  19. Departments of Anesthesiology and Critical Care Medicine, Neurology, and Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Robert D. Stevens

  20. Brown University, Providence, RI, USA

    Daniel Thengone

Authors
  1. Nicholas D. Schiff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Diringer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karin Diserens
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brian L. Edlow
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Olivia Gosseries
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. N. Jeremy Hill
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Leigh R. Hochberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fatima Y. Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ivo A. Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Charles B. Mikell
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sima Mofakham
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Erika Molteni
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Leonard Polizzotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Sudhin A. Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Robert D. Stevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Daniel Thengone
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

and the Curing Coma Campaign and its Contributing Members

Contributions

All authors contributed to the planning and structuring of the report. Authors contributed to the process through writing and regular biweekly meetings beginning January 2021 and continuously attended by subsets of the authors until the present time. The initial development of the group was directed by NS, RS, and MD; NS directed the project development and biweekly meetings. The manuscript has been approved by all authors.

Corresponding author

Correspondence to Nicholas D. Schiff.

Ethics declarations

Conflicts of interest

No authors report conflicts of interest with respect to this work product.

Ethical Approval/Informed Consent

The study did not involve new research involving human study participants or animal subjects and did not require any institutional review board or Institutional Animal Care and Use Committee oversight.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schiff, N.D., Diringer, M., Diserens, K. et al. Brain–Computer Interfaces for Communication in Patients with Disorders of Consciousness: A Gap Analysis and Scientific Roadmap. Neurocrit Care (2024). https://doi.org/10.1007/s12028-023-01924-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12028-023-01924-w

Keywords

Search

Navigation