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
Guideline seven: a proposal for standard montages to be used in clinical EEG. American Electroencephalographic Society. J Clin Neurophysiol. 1994;11(1):30–6.
Krauss GL, Fisher RS. The Johns Hopkins atlas of digital EEG: an interactive training guide. Baltimore: The Johns Hopkins University Press; 2006.
Brenner RP. The interpretation of the EEG in stupor and coma. Neurologist. 2005;11(5):271–84.
Young GB. The EEG in coma. J Clin Neurophysiol. 2000;17(5):473–85.
Posner JB, et al. The diagnosis of stupor and coma. 4th ed. New York: Oxford University Press; 2007.
Young GB, et al. An electroencephalographic classification for coma. Can J Neurol Sci. 1997;24(4):320–5.
Alvarez V, Rossetti AO. Clinical use of EEG in the ICU: technical setting. J Clin Neurophysiol. 2015;32(6):481–5.
Privitera M, et al. EEG detection of nontonic-clonic status epilepticus in patients with altered consciousness. Epilepsy Res. 1994;18(2):155–66.
Claassen J, et al. Detection of electrographic seizures with continuous EEG monitoring in critically ill patients. Neurology. 2004;62(10):1743–8.
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.
Kaplan PW. The clinical features, diagnosis, and prognosis of nonconvulsive status epilepticus. Neurologist. 2005;11(6):348–61.
Hockaday JM, et al. Electroencephalographic changes in acute cerebral anoxia from cardiac or respiratory arrest. Electroencephalogr Clin Neurophysiol. 1965;18:575–86.
Synek VM. Prognostically important EEG coma patterns in diffuse anoxic and traumatic encephalopathies in adults. J Clin Neurophysiol. 1988;5(2):161–74.
Rossetti AO, et al. Prognostication after cardiac arrest and hypothermia: a prospective study. Ann Neurol. 2010;67(3):301–7.
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.
Berkhoff M, Donati F, Bassetti C. Postanoxic alpha (theta) coma: a reappraisal of its prognostic significance. Clin Neurophysiol. 2000;111(2):297–304.
Westmoreland BF, et al. Alpha-coma. Electroencephalographic, clinical, pathologic, and etiologic correlations. Arch Neurol. 1975;32(11):713–8.
Guerit JM. Evoked potentials in severe brain injury. Prog Brain Res. 2005;150:415–26.
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.
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.
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
Robinson LR, et al. Predictive value of somatosensory evoked potentials for awakening from coma. Crit Care Med. 2003;31(3):960–7.
Cruccu G, et al. Recommendations for the clinical use of somatosensory-evoked potentials. Clin Neurophysiol. 2008;119(8):1705–19.
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.
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.
Zhang Y, et al. Predicting comatose patients with acute stroke outcome using middle-latency somatosensory evoked potentials. Clin Neurophysiol. 2011;122(8):1645–9.
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.
Haupt WF, Pawlik G, Thiel A. Initial and serial evoked potentials in cerebrovascular critical care patients. J Clin Neurophysiol. 2006;23(5):389–94.
Vanhaudenhuyse A, Laureys S, Perrin F. Cognitive event-related potentials in comatose and post-comatose states. Neurocrit Care. 2008;8(2):262–70.
Laureys S, et al. Residual cognitive function in comatose, vegetative and minimally conscious states. Curr Opin Neurol. 2005;18:726–33.
Fischer C, et al. Predictive value of sensory and cognitive evoked potentials for awakening from coma. Neurology. 2004;63(4):669–73.
Glass I, Sazbon L, Groswasser Z. Mapping “cognitive” event-related potentials in prolonged postcoma unawareness state. Clin Electroencephalogr. 1998;29(1):19–30.
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.
Mutschler V, et al. Auditory P300 in subjects in a post-anoxic coma. Preliminary data. Neurophysiol Clin. 1996;26(3):158–63.
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.
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.
Tzovara A, et al. Prediction of awakening from hypothermic post anoxic coma based on auditory discrimination. Ann Neurol. 2016; doi:10.1002/ana.24622.
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.
Munte TF, Heinze HJ. Brain potentials reveal deficits of language processing after closed head injury. Arch Neurol. 1994;51(5):482–93.
Granovsky Y, et al. P300 and stress in mild head injury patients. Electroencephalogr Clin Neurophysiol. 1998;108(6):554–9.
Pegado F, et al. Probing the lifetimes of auditory novelty detection processes. Neuropsychologia. 2010;48(10):3145–54.
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.
Schnakers C, et al. Voluntary brain processing in disorders of consciousness. Neurology. 2008;71:1614–20.
Yingling CD, Hosobuchi Y, Harrington M. P300 as a predictor of recovery from coma. Lancet. 1990;336(8719):873.
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.
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.
Thatcher RW. Validity and reliability of quantitative electroencephalography. J Neurother. 2010;14(2):122–52.
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.
Tzovara A, et al. Progression of auditory discrimination based on neural decoding predicts awakening from coma. Brain. 2013;136(Pt 1):81–9.
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.
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.
Rundgren M, et al. Continuous amplitude-integrated electroencephalogram predicts outcome in hypothermia-treated cardiac arrest patients. Crit Care Med. 2010;38(9):1838–44.
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.
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.
King JR, et al. Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness. Neuroimage. 2013;83C:726–38.
American Clinical Neurophysiology Society. Guideline 7: guidelines for writing EEG reports. J Clin Neurophysiol. 2006;23(2):118–21.
Estraneo A, et al. Standard EEG in diagnostic process of prolonged disorders of consciousness. Clin Neurophysiol. 2016;127(6):2379–85.
Kotchoubey B. First love does not die: a sustaining primacy effect on ERP components in an oddball paradigm. Brain Res. 2014;1556:38–45.
Kotchoubey B, et al. Information processing in severe disorders of consciousness: vegetative state and minimally conscious state. Clin Neurophysiol. 2005;116(10):2441–53.
Wijnen VJ, et al. Mismatch negativity predicts recovery from the vegetative state. Clin Neurophysiol. 2007;118(3):597–605.
Schnakers C, et al. Detecting consciousness in a total locked-in syndrome: an active event-related paradigm. Neurocase. 2009;4:1–7.
Real RG, et al. Information processing in patients in vegetative and minimally conscious states. Clin Neurophysiol. 2016;127(2):1395–402.
Chennu S, et al. Dissociable endogenous and exogenous attention in disorders of consciousness. Neuroimage Clin. 2013;3:450–61.
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.
Faugeras F, et al. Probing consciousness with event-related potentials in the vegetative state. Neurology. 2011;77(3):264–8.
King JR, et al. Information sharing in the brain indexes consciousness in noncommunicative patients. Curr Biol. 2013;23(19):1914–9.
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.
Kotchoubey B. Event-related potential measures of consciousness: two equations with three unknowns. Prog Brain Res. 2005;150:427–44.
Steppacher I, et al. N400 predicts recovery from disorders of consciousness. Ann Neurol. 2013;73(5):594–602.
Kubler A, Kotchoubey B. Brain-computer interfaces in the continuum of consciousness. Curr Opin Neurol. 2007;20(6):643–9.
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.
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.
Leon-Carrion J, et al. Brain function in the minimally conscious state: a quantitative neurophysiological study. Clin Neurophysiol. 2008;119(7):1506–14.
Pereda E, Quiroga RQ, Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol. 2005;77(1–2):1–37.
Laureys S. The neural correlate of (un)awareness: lessons from the vegetative state. Trends Cogn Sci. 2005;9:556–9.
Laureys S, et al. Impaired effective cortical connectivity in vegetative state: preliminary investigation using PET. Neuroimage. 1999;9(4):377–82.
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.
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.
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.
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.
Pollonini L, et al. Information communication networks in severe traumatic brain injury. Brain Topogr. 2010;23(2):221–6.
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.
Johansen JW, Sebel PS. Development and clinical application of electroencephalographic bispectrum monitoring. Anesthesiology. 2000;93(5):1336–44.
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.
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.
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.
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.
Holler Y, et al. Connectivity biomarkers can differentiate patients with different levels of consciousness. Clin Neurophysiol. 2014;125(8):1545–55.
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.
Bassetti CL, Aldrich MS. Sleep electroencephalogram changes in acute hemispheric stroke. Sleep Med. 2001;2(3):185–94.
Crowley K, et al. Differentiating pathologic delta from healthy physiologic delta in patients with Alzheimer disease. Sleep. 2005;28(7):865–70.
Cologan V, et al. Sleep in disorders of consciousness. Sleep Med Rev. 2010;14(2):97–105.
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.
Malinowska U, et al. Electroencephalographic profiles for differentiation of disorders of consciousness. Biomed Eng Online. 2013;12(1):109.
Cologan, V., et al., Sleep in the unresponsive wakefulness syndrome and minimally conscious state. J Neurotrauma, 2012.
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.
Bekinschtein TA, et al. Can electromyography objectively detect voluntary movement in disorders of consciousness? J Neurol Neurosurg Psychiatry. 2008;79(7):826–8.
Habbal D, et al. Volitional electromyographic responses in disorders of consciousness. Brain Inj. 2014;28(9):1171–9.
Lesenfants D, et al. Electromyographic decoding of response to command in disorders of consciousness. Neurology. 2016;87(20):2099–107.
Wolpaw JR, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–91.
Schnakers C, et al. Cognitive function in the locked-in syndrome. J Neurol. 2008;255(3):323–30.
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.
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.
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.
Owen AM, et al. Detecting awareness in the vegetative state. Science. 2006;313(5792):1402.
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.
Sellers EW, Donchin E. A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol. 2006;117(3):538–48.
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.
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.
Citi L, et al. P300-based BCI mouse with genetically-optimized analogue control. IEEE Trans Neural Syst Rehabil Eng. 2008;16(1):51–61.
Yoo SS, et al. Brain-computer interface using fMRI: spatial navigation by thoughts. Neuroreport. 2004;15(10):1591–5.
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.
Sellers, E.W., T.M. Vaughan, and J.R. Wolpaw, A brain-computer interface for long-term independent home use. Amyotroph Lateral Scler, 2010.
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.
Nijboer F, et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol. 2008;119(8):1909–16.
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.
Furdea A, et al. An auditory oddball (P300) spelling system for brain-computer interfaces. Psychophysiology. 2009;46(3):617–25.
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.
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.
Lesenfants D, et al. An independent SSVEP-based brain-computer interface in locked-in syndrome. J Neural Eng. 2014;11(3):035002.
Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842–57.
Pfurtscheller G, et al. EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol. 1997;103(6):642–51.
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.
Perelmouter J, et al. Language support program for thought translation devices. Automedica. 1999;18:67–84.
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.
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.
Cruse D, et al. Bedside detection of awareness in the vegetative state. Lancet. 2011;378(9809):2088–94.
Cruse D, et al. The relationship between aetiology and covert cognition in the minimally-conscious state. Neurology. 2012;78(11):816–22.
Goldfine AM, et al. Reanalysis of bedside detection of awareness in the vegetative state: a cohort study. Lancet. 2013;381(9863):289–91.
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.
Cruse D, et al. Detecting awareness in the vegetative state: electroencephalographic evidence for attempted movements to command. PLoS One. 2012;7(11):e49933.
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.
Kennedy PR, Bakay RA. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport. 1998;9(8):1707–11.
Kennedy PR, et al. Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng. 2000;8(2):198–202.
Hochberg LR, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 2012;485(7398):372–5.
Hochberg LR, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006;442(7099):164–71.
Brumberg JS, et al. Brain-computer interfaces for speech communication. Speech Commun. 2010;52(4):367–79.
Hinterberger T, et al. Voluntary brain regulation and communication with electrocorticogram signals. Epilepsy Behav. 2008;13(2):300–6.
Leuthardt EC, et al. A brain-computer interface using electrocorticographic signals in humans. J Neural Eng. 2004;1(2):63–71.
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.
Noirhomme Q, et al. Look at my classifier’s result: disentangling unresponsive from (minimally) conscious patients. Neuroimage. 2017;145(Pt B):288–303.
Giacino J, et al. The minimally conscious state: definition and diagnostic criteria. Neurology. 2002;58(3):349–53.
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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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