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Electrophysiology in Disorders of Consciousness: From Conventional EEG Visual Analysis to Brain-Computer Interfaces

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Coma and Disorders of Consciousness

Abstract

Electroencephalography can offer many insights into brain activity useful for the study of disorders of consciousness. In this chapter, we will focus on the state of knowledge regarding the implementation of such a technique for diagnosis and prognosis in clinical setting, as well as the current effort for developing more reliable methods for assessing severely brain-injured patients with altered state of consciousness.

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References

  1. Guideline seven: a proposal for standard montages to be used in clinical EEG. American Electroencephalographic Society. J Clin Neurophysiol. 1994;11(1):30–6.

    Google Scholar 

  2. Krauss GL, Fisher RS. The Johns Hopkins atlas of digital EEG: an interactive training guide. Baltimore: The Johns Hopkins University Press; 2006.

    Google Scholar 

  3. Brenner RP. The interpretation of the EEG in stupor and coma. Neurologist. 2005;11(5):271–84.

    Article  PubMed  Google Scholar 

  4. Young GB. The EEG in coma. J Clin Neurophysiol. 2000;17(5):473–85.

    Article  CAS  PubMed  Google Scholar 

  5. Posner JB, et al. The diagnosis of stupor and coma. 4th ed. New York: Oxford University Press; 2007.

    Google Scholar 

  6. Young GB, et al. An electroencephalographic classification for coma. Can J Neurol Sci. 1997;24(4):320–5.

    Article  CAS  PubMed  Google Scholar 

  7. Alvarez V, Rossetti AO. Clinical use of EEG in the ICU: technical setting. J Clin Neurophysiol. 2015;32(6):481–5.

    Article  PubMed  Google Scholar 

  8. Privitera M, et al. EEG detection of nontonic-clonic status epilepticus in patients with altered consciousness. Epilepsy Res. 1994;18(2):155–66.

    Article  CAS  PubMed  Google Scholar 

  9. Claassen J, et al. Detection of electrographic seizures with continuous EEG monitoring in critically ill patients. Neurology. 2004;62(10):1743–8.

    Article  CAS  PubMed  Google Scholar 

  10. Woo Lee J. Which EEG patterns deserve treatment in the ICU? In: Rossetti A, Laureys S, editors. Clinical neurophysiology in disorders of consciousness: brain function monitoring in the ICU and beyond. Wien: Springer; 2015.

    Google Scholar 

  11. Kaplan PW. The clinical features, diagnosis, and prognosis of nonconvulsive status epilepticus. Neurologist. 2005;11(6):348–61.

    Article  PubMed  Google Scholar 

  12. Hockaday JM, et al. Electroencephalographic changes in acute cerebral anoxia from cardiac or respiratory arrest. Electroencephalogr Clin Neurophysiol. 1965;18:575–86.

    Article  CAS  PubMed  Google Scholar 

  13. Synek VM. Prognostically important EEG coma patterns in diffuse anoxic and traumatic encephalopathies in adults. J Clin Neurophysiol. 1988;5(2):161–74.

    Article  CAS  PubMed  Google Scholar 

  14. Rossetti AO, et al. Prognostication after cardiac arrest and hypothermia: a prospective study. Ann Neurol. 2010;67(3):301–7.

    PubMed  Google Scholar 

  15. Rossetti AO. Prognostic utility of electroencephalogram in acute consciousness impairment. In: Rossetti AO, Laureys S, editors. Clinical neurophysiology in disorders of consciousness. New York: Springer; 2015.

    Chapter  Google Scholar 

  16. Berkhoff M, Donati F, Bassetti C. Postanoxic alpha (theta) coma: a reappraisal of its prognostic significance. Clin Neurophysiol. 2000;111(2):297–304.

    Article  CAS  PubMed  Google Scholar 

  17. Westmoreland BF, et al. Alpha-coma. Electroencephalographic, clinical, pathologic, and etiologic correlations. Arch Neurol. 1975;32(11):713–8.

    Article  CAS  PubMed  Google Scholar 

  18. Guerit JM. Evoked potentials in severe brain injury. Prog Brain Res. 2005;150:415–26.

    Article  PubMed  Google Scholar 

  19. Amantini A, et al. Prediction of 'awakening' and outcome in prolonged acute coma from severe traumatic brain injury: evidence for validity of short latency SEPs. Clin Neurophysiol. 2005;116(1):229–35.

    Article  PubMed  Google Scholar 

  20. Fischer C, et al. Improved prediction of awakening or nonawakening from severe anoxic coma using tree-based classification analysis. Crit Care Med. 2006;34(5):1520–4.

    Article  PubMed  Google Scholar 

  21. Lew HL, et al. Use of somatosensory-evoked potentials and cognitive event-related potentials in predicting outcomes of patients with severe traumatic brain injury. Am J Phys Med Rehabil. 2003;82(1):53–61. quiz 62–4, 80

    Article  PubMed  Google Scholar 

  22. Robinson LR, et al. Predictive value of somatosensory evoked potentials for awakening from coma. Crit Care Med. 2003;31(3):960–7.

    Article  PubMed  Google Scholar 

  23. Cruccu G, et al. Recommendations for the clinical use of somatosensory-evoked potentials. Clin Neurophysiol. 2008;119(8):1705–19.

    Article  CAS  PubMed  Google Scholar 

  24. Tjepkema-Cloostermans M, van Putten M, Horn J. Prognostic use of somatosensory evoked potentials in acute consciousness impairment. In: Rossetti A, Laureys S, editors. Clinical neurophysiology in disorders of consciousness. Wien: Srpinger; 2015.

    Google Scholar 

  25. Su YY, et al. Parameters and grading of evoked potentials: prediction of unfavorable outcome in patients with severe stroke. J Clin Neurophysiol. 2010;27(1):25–9.

    Article  PubMed  Google Scholar 

  26. Zhang Y, et al. Predicting comatose patients with acute stroke outcome using middle-latency somatosensory evoked potentials. Clin Neurophysiol. 2011;122(8):1645–9.

    Article  PubMed  Google Scholar 

  27. de Sousa LC, et al. Auditory brainstem response: prognostic value in patients with a score of 3 on the Glasgow Coma Scale. Otol Neurotol. 2007;28(3):426–8.

    PubMed  Google Scholar 

  28. Haupt WF, Pawlik G, Thiel A. Initial and serial evoked potentials in cerebrovascular critical care patients. J Clin Neurophysiol. 2006;23(5):389–94.

    Article  PubMed  Google Scholar 

  29. Vanhaudenhuyse A, Laureys S, Perrin F. Cognitive event-related potentials in comatose and post-comatose states. Neurocrit Care. 2008;8(2):262–70.

    Article  PubMed  Google Scholar 

  30. Laureys S, et al. Residual cognitive function in comatose, vegetative and minimally conscious states. Curr Opin Neurol. 2005;18:726–33.

    Article  PubMed  Google Scholar 

  31. Fischer C, et al. Predictive value of sensory and cognitive evoked potentials for awakening from coma. Neurology. 2004;63(4):669–73.

    Article  PubMed  Google Scholar 

  32. Glass I, Sazbon L, Groswasser Z. Mapping “cognitive” event-related potentials in prolonged postcoma unawareness state. Clin Electroencephalogr. 1998;29(1):19–30.

    Article  CAS  PubMed  Google Scholar 

  33. Guerit JM, et al. ERPs obtained with the auditory oddball paradigm in coma and altered states of consciousness: clinical relationships, prognostic value, and origin of components. Clin Neurophysiol. 1999;110(7):1260–9.

    Article  CAS  PubMed  Google Scholar 

  34. Mutschler V, et al. Auditory P300 in subjects in a post-anoxic coma. Preliminary data. Neurophysiol Clin. 1996;26(3):158–63.

    Google Scholar 

  35. Kane NM, et al. Event-related potentials--neurophysiological tools for predicting emergence and early outcome from traumatic coma. Intensive Care Med. 1996;22(1):39–46.

    Article  CAS  PubMed  Google Scholar 

  36. Naccache L, et al. Auditory mismatch negativity is a good predictor of awakening in comatose patients: a fast and reliable procedure. Clin Neurophysiol. 2005;116(4):988–9.

    Article  PubMed  Google Scholar 

  37. Tzovara A, et al. Prediction of awakening from hypothermic post anoxic coma based on auditory discrimination. Ann Neurol. 2016; doi:10.1002/ana.24622.

    PubMed  Google Scholar 

  38. Rossetti AO, et al. Automated auditory mismatch negativity paradigm improves coma prognostic accuracy after cardiac arrest and therapeutic hypothermia. J Clin Neurophysiol. 2014;31(4):356–61.

    Article  PubMed  Google Scholar 

  39. Munte TF, Heinze HJ. Brain potentials reveal deficits of language processing after closed head injury. Arch Neurol. 1994;51(5):482–93.

    Article  CAS  PubMed  Google Scholar 

  40. Granovsky Y, et al. P300 and stress in mild head injury patients. Electroencephalogr Clin Neurophysiol. 1998;108(6):554–9.

    Article  CAS  PubMed  Google Scholar 

  41. Pegado F, et al. Probing the lifetimes of auditory novelty detection processes. Neuropsychologia. 2010;48(10):3145–54.

    Article  PubMed  Google Scholar 

  42. Perrin F, et al. Brain response to one’s own name in vegetative state, minimally conscious state, and locked-in syndrome. Arch Neurol. 2006;63:562–9.

    Article  PubMed  Google Scholar 

  43. Schnakers C, et al. Voluntary brain processing in disorders of consciousness. Neurology. 2008;71:1614–20.

    Article  CAS  PubMed  Google Scholar 

  44. Yingling CD, Hosobuchi Y, Harrington M. P300 as a predictor of recovery from coma. Lancet. 1990;336(8719):873.

    Article  CAS  PubMed  Google Scholar 

  45. Gott PS, Rabinowicz AL, DeGiorgio CM. P300 auditory event-related potentials in nontraumatic coma. Association with Glasgow Coma Score and awakening. Arch Neurol. 1991;48(12):1267–70.

    Article  CAS  PubMed  Google Scholar 

  46. Fischer C, Dailler F, Morlet D. Novelty P3 elicited by the subject's own name in comatose patients. Clin Neurophysiol. 2008;119(10):2224–30.

    Article  PubMed  Google Scholar 

  47. Thatcher RW. Validity and reliability of quantitative electroencephalography. J Neurother. 2010;14(2):122–52.

    Article  Google Scholar 

  48. Forgacs PB, et al. Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following. Ann Neurol. 2014;76(6):869–79.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Tzovara A, et al. Progression of auditory discrimination based on neural decoding predicts awakening from coma. Brain. 2013;136(Pt 1):81–9.

    Article  PubMed  Google Scholar 

  50. Wennervirta JE, et al. Hypothermia-treated cardiac arrest patients with good neurological outcome differ early in quantitative variables of EEG suppression and epileptiform activity. Crit Care Med. 2009;37(8):2427–35.

    Article  PubMed  Google Scholar 

  51. Rundgren M, Rosen I, Friberg H. Amplitude-integrated EEG (aEEG) predicts outcome after cardiac arrest and induced hypothermia. Intensive Care Med. 2006;32(6):836–42.

    Article  PubMed  Google Scholar 

  52. Rundgren M, et al. Continuous amplitude-integrated electroencephalogram predicts outcome in hypothermia-treated cardiac arrest patients. Crit Care Med. 2010;38(9):1838–44.

    Article  PubMed  Google Scholar 

  53. Noirhomme Q, et al. Automated analysis of background EEG and reactivity during therapeutic hypothermia in comatose patients after cardiac arrest. Clin EEG Neurosci. 2014;45(1):6–13.

    Article  PubMed  Google Scholar 

  54. Sitt JD, et al. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. Brain. 2014;137(Pt 8):2258–70.

    Article  PubMed  PubMed Central  Google Scholar 

  55. King JR, et al. Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness. Neuroimage. 2013;83C:726–38.

    Article  Google Scholar 

  56. American Clinical Neurophysiology Society. Guideline 7: guidelines for writing EEG reports. J Clin Neurophysiol. 2006;23(2):118–21.

    Article  Google Scholar 

  57. Estraneo A, et al. Standard EEG in diagnostic process of prolonged disorders of consciousness. Clin Neurophysiol. 2016;127(6):2379–85.

    Article  PubMed  Google Scholar 

  58. Kotchoubey B. First love does not die: a sustaining primacy effect on ERP components in an oddball paradigm. Brain Res. 2014;1556:38–45.

    Article  CAS  PubMed  Google Scholar 

  59. Kotchoubey B, et al. Information processing in severe disorders of consciousness: vegetative state and minimally conscious state. Clin Neurophysiol. 2005;116(10):2441–53.

    Article  CAS  PubMed  Google Scholar 

  60. Wijnen VJ, et al. Mismatch negativity predicts recovery from the vegetative state. Clin Neurophysiol. 2007;118(3):597–605.

    Article  CAS  PubMed  Google Scholar 

  61. Schnakers C, et al. Detecting consciousness in a total locked-in syndrome: an active event-related paradigm. Neurocase. 2009;4:1–7.

    Google Scholar 

  62. Real RG, et al. Information processing in patients in vegetative and minimally conscious states. Clin Neurophysiol. 2016;127(2):1395–402.

    Article  PubMed  Google Scholar 

  63. Chennu S, et al. Dissociable endogenous and exogenous attention in disorders of consciousness. Neuroimage Clin. 2013;3:450–61.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Pokorny C, et al. The auditory P300-based single-switch brain-computer interface: paradigm transition from healthy subjects to minimally conscious patients. Artif Intell Med. 2013;59(2):81–90.

    Article  PubMed  Google Scholar 

  65. Faugeras F, et al. Probing consciousness with event-related potentials in the vegetative state. Neurology. 2011;77(3):264–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. King JR, et al. Information sharing in the brain indexes consciousness in noncommunicative patients. Curr Biol. 2013;23(19):1914–9.

    Article  CAS  PubMed  Google Scholar 

  67. Bekinschtein TA, et al. Neural signature of the conscious processing of auditory regularities. Proc Natl Acad Sci U S A. 2009;106(5):1672–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kotchoubey B. Event-related potential measures of consciousness: two equations with three unknowns. Prog Brain Res. 2005;150:427–44.

    Article  PubMed  Google Scholar 

  69. Steppacher I, et al. N400 predicts recovery from disorders of consciousness. Ann Neurol. 2013;73(5):594–602.

    Article  PubMed  Google Scholar 

  70. Kubler A, Kotchoubey B. Brain-computer interfaces in the continuum of consciousness. Curr Opin Neurol. 2007;20(6):643–9.

    Article  PubMed  Google Scholar 

  71. Lehembre R, et al. Resting-state EEG study of comatose patients: a connectivity and frequency analysis to find differences between vegetative and minimally conscious states. Funct Neurol. 2012;27(1):41–7.

    PubMed  PubMed Central  Google Scholar 

  72. Lechinger J, et al. CRS-R score in disorders of consciousness is strongly related to spectral EEG at rest. J Neurol. 2013;260(9):2348–56.

    Article  PubMed  Google Scholar 

  73. Leon-Carrion J, et al. Brain function in the minimally conscious state: a quantitative neurophysiological study. Clin Neurophysiol. 2008;119(7):1506–14.

    Article  CAS  PubMed  Google Scholar 

  74. Pereda E, Quiroga RQ, Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol. 2005;77(1–2):1–37.

    Article  PubMed  Google Scholar 

  75. Laureys S. The neural correlate of (un)awareness: lessons from the vegetative state. Trends Cogn Sci. 2005;9:556–9.

    Article  PubMed  Google Scholar 

  76. Laureys S, et al. Impaired effective cortical connectivity in vegetative state: preliminary investigation using PET. Neuroimage. 1999;9(4):377–82.

    Article  CAS  PubMed  Google Scholar 

  77. Vanhaudenhuyse A, et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain. 2010;133(Pt 1):161–71.

    Article  PubMed  Google Scholar 

  78. Soddu A, et al. Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness. Hum Brain Mapp. 2012;33(4):778–96.

    Article  PubMed  Google Scholar 

  79. Davey MP, Victor JD, Schiff ND. Power spectra and coherence in the EEG of a vegetative patient with severe asymmetric brain damage. Clin Neurophysiol. 2000;111(11):1949–54.

    Article  CAS  PubMed  Google Scholar 

  80. Schiff N Large scale brain dynamics and connectivity in the minimally conscious state. In Handbook of brain connectivity. New York: Springer; 2007. p. 505–20.

    Google Scholar 

  81. Pollonini L, et al. Information communication networks in severe traumatic brain injury. Brain Topogr. 2010;23(2):221–6.

    Article  PubMed  Google Scholar 

  82. Fingelkurts AA, et al. EEG oscillatory states as neuro-phenomenology of consciousness as revealed from patients in vegetative and minimally conscious states. Conscious Cogn. 2012;21(1):149–69.

    Article  PubMed  Google Scholar 

  83. Johansen JW, Sebel PS. Development and clinical application of electroencephalographic bispectrum monitoring. Anesthesiology. 2000;93(5):1336–44.

    Article  CAS  PubMed  Google Scholar 

  84. Noirhomme Q, et al. Bispectral index correlates with regional cerebral blood flow during sleep in distinct cortical and subcortical structures in humans. Arch Ital Biol. 2009;147(1–2):51–7.

    CAS  PubMed  Google Scholar 

  85. Schnakers C, et al. Diagnostic and prognostic use of bispectral index in coma, vegetative state and related disorders. Brain Inj. 2008;22(12):926–31.

    Article  CAS  PubMed  Google Scholar 

  86. Gosseries O, et al. Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state. Funct Neurol. 2011;26(1):25–30.

    PubMed  PubMed Central  Google Scholar 

  87. Viertio-Oja H, et al. Description of the entropy algorithm as applied in the Datex-Ohmeda S/5 entropy module. Acta Anaesthesiol Scand. 2004;48(2):154–61.

    Article  CAS  PubMed  Google Scholar 

  88. Holler Y, et al. Connectivity biomarkers can differentiate patients with different levels of consciousness. Clin Neurophysiol. 2014;125(8):1545–55.

    Article  PubMed  Google Scholar 

  89. Riedner BA, et al. Sleep homeostasis and cortical synchronization: III. A high-density EEG study of sleep slow waves in humans. Sleep. 2007;30(12):1643–57.

    Google Scholar 

  90. Bassetti CL, Aldrich MS. Sleep electroencephalogram changes in acute hemispheric stroke. Sleep Med. 2001;2(3):185–94.

    Article  PubMed  Google Scholar 

  91. Crowley K, et al. Differentiating pathologic delta from healthy physiologic delta in patients with Alzheimer disease. Sleep. 2005;28(7):865–70.

    Article  PubMed  Google Scholar 

  92. Cologan V, et al. Sleep in disorders of consciousness. Sleep Med Rev. 2010;14(2):97–105.

    Article  PubMed  Google Scholar 

  93. Landsness E, et al. Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state. Brain. 2011;134(Pt 8):2222–32.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Malinowska U, et al. Electroencephalographic profiles for differentiation of disorders of consciousness. Biomed Eng Online. 2013;12(1):109.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Cologan, V., et al., Sleep in the unresponsive wakefulness syndrome and minimally conscious state. J Neurotrauma, 2012.

    Google Scholar 

  96. Arnaldi D, et al. The prognostic value of sleep patterns in disorders of consciousness in the sub-acute phase. Clin Neurophysiol. 2016;127(2):1445–51.

    Article  PubMed  Google Scholar 

  97. Bekinschtein TA, et al. Can electromyography objectively detect voluntary movement in disorders of consciousness? J Neurol Neurosurg Psychiatry. 2008;79(7):826–8.

    Article  CAS  PubMed  Google Scholar 

  98. Habbal D, et al. Volitional electromyographic responses in disorders of consciousness. Brain Inj. 2014;28(9):1171–9.

    Article  PubMed  Google Scholar 

  99. Lesenfants D, et al. Electromyographic decoding of response to command in disorders of consciousness. Neurology. 2016;87(20):2099–107.

    Article  PubMed  Google Scholar 

  100. Wolpaw JR, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–91.

    Article  PubMed  Google Scholar 

  101. Schnakers C, et al. Cognitive function in the locked-in syndrome. J Neurol. 2008;255(3):323–30.

    Article  PubMed  Google Scholar 

  102. Ball LJ, Fager S, Fried-Oken M. Augmentative and alternative communication for people with progressive neuromuscular disease. Phys Med Rehabil Clin N Am. 2012;23(3):689–99.

    Article  PubMed  Google Scholar 

  103. Bruno MA, et al. Locked-in syndrome in children: report of five cases and review of the literature. Pediatr Neurol. 2009;41(4):237–46.

    Article  PubMed  Google Scholar 

  104. Kubler A, Neumann N. Brain-computer interfaces - the key for the conscious brain locked into a paralyzed body. Prog Brain Res. 2005;150:513–25.

    Google Scholar 

  105. Owen AM, et al. Detecting awareness in the vegetative state. Science. 2006;313(5792):1402.

    Article  CAS  PubMed  Google Scholar 

  106. Sorger B, et al. Another kind of 'BOLD response’: answering multiple-choice questions via online decoded single-trial brain signals. Prog Brain Res. 2009;177:275–92.

    Article  PubMed  Google Scholar 

  107. Sellers EW, Donchin E. A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol. 2006;117(3):538–48.

    Article  PubMed  Google Scholar 

  108. Sellers EW, Kubler A, Donchin E. Brain-computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: the P300 speller. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):221–4.

    Article  PubMed  Google Scholar 

  109. Kübler A. Brain-computer interfaces for communication in paralysed patients and implications for disorders of consciousness. In: Laureys S, Tononi G, editors. The neurology of consciousness. New York: Academic Press; 2009. p. 217–34.

    Chapter  Google Scholar 

  110. Citi L, et al. P300-based BCI mouse with genetically-optimized analogue control. IEEE Trans Neural Syst Rehabil Eng. 2008;16(1):51–61.

    Article  PubMed  Google Scholar 

  111. Yoo SS, et al. Brain-computer interface using fMRI: spatial navigation by thoughts. Neuroreport. 2004;15(10):1591–5.

    Article  PubMed  Google Scholar 

  112. Mugler, E.M., et al., Design and implementation of a P300-based brain-computer interface for controlling an internet browser. IEEE Trans Neural Syst Rehabil Eng, 2010.

    Google Scholar 

  113. Sellers, E.W., T.M. Vaughan, and J.R. Wolpaw, A brain-computer interface for long-term independent home use. Amyotroph Lateral Scler, 2010.

    Google Scholar 

  114. Lee JH, et al. Brain-machine interface via real-time fMRI: preliminary study on thought-controlled robotic arm. Neurosci Lett. 2009;450(1):1–6.

    Article  CAS  PubMed  Google Scholar 

  115. Nijboer F, et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol. 2008;119(8):1909–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Donchin E, Spencer KM, Wijesinghe R. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil Eng. 2000;8(2):174–9.

    Article  CAS  PubMed  Google Scholar 

  117. Furdea A, et al. An auditory oddball (P300) spelling system for brain-computer interfaces. Psychophysiology. 2009;46(3):617–25.

    Article  CAS  PubMed  Google Scholar 

  118. Lule D, et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clin Neurophysiol. 2013;124(1):101–6.

    Article  PubMed  Google Scholar 

  119. Combaz A, et al. A comparison of two spelling brain-computer interfaces based on visual P3 and SSVEP in locked-in syndrome. PLoS One. 2013;8(9):e73691.

    Google Scholar 

  120. Lesenfants D, et al. An independent SSVEP-based brain-computer interface in locked-in syndrome. J Neural Eng. 2014;11(3):035002.

    Google Scholar 

  121. Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842–57.

    Article  CAS  PubMed  Google Scholar 

  122. Pfurtscheller G, et al. EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol. 1997;103(6):642–51.

    Article  CAS  PubMed  Google Scholar 

  123. Neuper C, et al. Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol. 2003;114(3):399–409.

    Article  CAS  PubMed  Google Scholar 

  124. Perelmouter J, et al. Language support program for thought translation devices. Automedica. 1999;18:67–84.

    Google Scholar 

  125. Pfurtscheller G, et al. 15 years of BCI research at Graz University of Technology: current projects. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):205–10.

    Article  CAS  PubMed  Google Scholar 

  126. Goldfine AM, et al. Determination of awareness in patients with severe brain injury using EEG power spectral analysis. Clin Neurophysiol. 2011;122(11):2157–68.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Cruse D, et al. Bedside detection of awareness in the vegetative state. Lancet. 2011;378(9809):2088–94.

    Article  PubMed  Google Scholar 

  128. Cruse D, et al. The relationship between aetiology and covert cognition in the minimally-conscious state. Neurology. 2012;78(11):816–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Goldfine AM, et al. Reanalysis of bedside detection of awareness in the vegetative state: a cohort study. Lancet. 2013;381(9863):289–91.

    Article  PubMed  PubMed Central  Google Scholar 

  130. Cruse D, et al. Reanalysis of “Bedside detection of awareness in the vegetative state: a cohort study” – authors’ reply. Lancet. 2013;381(9863):291–2.

    Google Scholar 

  131. Cruse D, et al. Detecting awareness in the vegetative state: electroencephalographic evidence for attempted movements to command. PLoS One. 2012;7(11):e49933.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Pan J, et al. Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface. J Neural Eng. 2014;11(5):056007.

    Article  PubMed  Google Scholar 

  133. Kennedy PR, Bakay RA. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport. 1998;9(8):1707–11.

    Article  CAS  PubMed  Google Scholar 

  134. Kennedy PR, et al. Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng. 2000;8(2):198–202.

    Article  CAS  PubMed  Google Scholar 

  135. Hochberg LR, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 2012;485(7398):372–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Hochberg LR, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006;442(7099):164–71.

    Article  CAS  PubMed  Google Scholar 

  137. Brumberg JS, et al. Brain-computer interfaces for speech communication. Speech Commun. 2010;52(4):367–79.

    Article  PubMed  PubMed Central  Google Scholar 

  138. Hinterberger T, et al. Voluntary brain regulation and communication with electrocorticogram signals. Epilepsy Behav. 2008;13(2):300–6.

    Article  PubMed  Google Scholar 

  139. Leuthardt EC, et al. A brain-computer interface using electrocorticographic signals in humans. J Neural Eng. 2004;1(2):63–71.

    Article  PubMed  Google Scholar 

  140. Jarosiewicz B, et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Sci Transl Med. 2015;7(313):313ra179.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Noirhomme Q, et al. Look at my classifier’s result: disentangling unresponsive from (minimally) conscious patients. Neuroimage. 2017;145(Pt B):288–303.

    Article  PubMed  Google Scholar 

  142. Giacino J, et al. The minimally conscious state: definition and diagnostic criteria. Neurology. 2002;58(3):349–53.

    Article  PubMed  Google Scholar 

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Chatelle, C., Lesenfants, D., Noirhomme, Q. (2018). Electrophysiology in Disorders of Consciousness: From Conventional EEG Visual Analysis to Brain-Computer Interfaces. In: Schnakers, C., Laureys, S. (eds) Coma and Disorders of Consciousness. Springer, Cham. https://doi.org/10.1007/978-3-319-55964-3_4

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