brain-computer interface; disorders of consciousness; diagnosis
Research Center/Unit :
GIGA CRC (Cyclotron Research Center) In vivo Imaging-Aging & Memory - ULiège
Disciplines :
Neurology
Author, co-author :
Chatelle, Camille ; Université de Liège - ULiège > Centre de recherches du cyclotron
Lugo, zulay
Noirhomme, Quentin ; Université de Liège - ULiège > Centre de recherches du cyclotron
Sorger, Bettina
Lulé, Dorothée
Language :
English
Title :
Brain-Computer Interface: A Communication Aid?
Publication date :
2012
Main work title :
Coma and Disorders of Consciousness
Editor :
Schnakers, Caroline ; Université de Liège - ULiège > Unités de recherche interfacultaires > GIGA-CRC In vivo Imaging (Centre de Recherche du Cyclotron)
Laureys, Steven ; Centre Hospitalier Universitaire de Liège - CHU > Centre du Cerveau²
Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767-91.
Sorger B, Dahmen B, Reithler J, 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. London/Burlington/San Diego: Academic Press/Elsevier; 2008. p. 217-34.
Citi L, Poli R, Cinel C, Sepulveda F. P300-based BCI mouse with genetically-optimized analogue control. IEEE Trans Neural Syst Rehabil Eng. 2008;16(1):51-61.
Yoo SS, Fairneny T, Chen NK, et al. Brain-computer interface using fMRI: spatial navigation by thoughts. Neuroreport. 2004;15(10):1591-5.
Mugler EM, Ruf CA, Halder S, et al. Design and implementation of a P300-based brain-computer interface for controlling an internet browser. IEEE Trans Neural Syst Rehabil Eng. 2010;18:599-609.
Sellers EW, Vaughan TM, Wolpaw JR. A brain-computer interface for long-term independent home use. Amyotroph Lateral Scler. 2010;11:449-55.
Lee JH, Ryu J, Jolesz FA, et al. Brain-machine interface via real-time fMRI: preliminary study on thought-controlled robotic arm. Neurosci Lett. 2009;450(1):1-6.
Schnakers C, Majerus S, Goldman S, et al. Cognitive function in the locked-in syndrome. J Neurol. 2008;255(3):323-30.
Bruno MA, Schnakers C, Damas F, 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, Coleman MR, Boly M, et al. Detecting awareness in the vegetative state. Science. 2006;313(5792):1402.
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, Halder S, Krusienski DJ, et al. An auditory oddball (P300) spelling system for brain-computer interfaces. Psychophysiology. 2009;46(3):617-25.
Kubler A, Furdea A, Halder S, et al. A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Ann N Y Acad Sci. 2009; 1157:90-100.
Schreuder M, Blankertz B, Tangermann M. A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS One. 2010;5(4):e9813.
Halder S, Rea M, Andreoni R, et al. An auditory oddball brain-computer interface for binary choices. Clin Neurophysiol. 2010;121(4):516-23.
Regan D. Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalogr Clin Neurophysiol. 1966;20(3):238-48.
Vialatte FB, Maurice M, Dauwels J, Cichocki A. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog Neurobiol. 2010;90(4):418-38.
Cecotti H. A self-paced and calibration-less SSVEP-based brain-computer interface speller. IEEE Trans Neural Syst Rehabil Eng. 2010;18(2):127-33.
Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842-57.
Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M. EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol. 1997; 103(6):642-51.
Scherer R, Muller GR, Neuper C, et al. An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans Biomed Eng. 2004;51(6):979-84.
Nijboer F, Furdea A, Gunst I, et al. An auditory brain-computer interface (BCI). J Neurosci Methods. 2008;167(1):43-50.
Birbaumer N. Slow cortical potentials: their origin, meaning, and clinical use. In: van Boxtel GJM, Bocker KBE, editors. Brain and behavior past, present, and future. Tilburg: Tilburg University Press; 1997. p. 25-39.
Elbert T, Rockstroh B, Lutzenberger W, Birbaumer N. Biofeedback of slow cortical potentials. I. Electroencephalogr Clin Neurophysiol. 1980;48(3):293-301.
Vidal JJ. Toward direct brain-computer communication. Annu Rev Biophys Bioeng. 1973;2:157-80.
Thut G, Nietzel A, Brandt SA, Pascual-Leone A. Alpha-band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. J Neurosci. 2006;26(37):9494-502.
Kelly SP, Lalor EC, Reilly RB, Foxe JJ. Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention. J Neurophysiol. 2006;95(6):3844-51.
van Gerven M, Jensen O. Attention modulations of posterior alpha as a control signal for two-dimensional brain-computer interfaces. J Neurosci Methods. 2009;179(1):78-84.
Kwong KK, Belliveau JW, Chesler DA, et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci USA. 1992;89(12):5675-9.
Boly M, Coleman MR, Davis MH, et al. When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients. Neuroimage. 2007;36(3):979-92.
Monti M, Colemand MR, Owen AM. "Brain-reading" with real-time fMRI: communication via detection of brain states in the absence of motor response. In: 14th annual meeting of the organization for the human brain mapping. Melbourne: Elsevier; 2008. p. 133.
Irani F, Platek SM, Bunce S, et al. Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders. Clin Neuropsychol. 2007;21(1):9-37.
Coyle SM, Ward TE, Markham CM. Brain-computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng. 2007;4(3):219-26.
Luu S, Chau T. Decoding subjective preference from single-trial near-infrared spectroscopy signals. J Neural Eng. 2009;6(1):016003.
Sitaram R, Zhang H, Guan C, et al. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. Neuroimage. 2007;34(4):1416-27.
Nijboer F, Sellers EW, Mellinger J, et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol. 2008;119(8):1909-16.
Lulé D, Noirhomme Q, Kleih S, et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clin Neurophysiol (Accepted).
Kubler A, Kotchoubey B, Hinterberger T, et al. The thought translation device: a neurophysiological approach to communication in total motor paralysis. Exp Brain Res. 1999;124(2): 223-32.
Neuper C, Muller GR, Kubler A, 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, Kotchoubey B, Kübler A, et al. Language support program for thought translation devices. Automedica. 1999;18:67-84.
Pfurtscheller G, Muller-Putz GR, Schlogl A, 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.
Cruse D, Bekinschtein TA, Monti M, Owen AM. Detecting awareness in the vegetative state with EEG. In: 16th annual meeting of the organization for human brain mapping. Barcelona: Elsevier; 2010.
Birbaumer N, Ghanayim N, Hinterberger T, et al. A spelling device for the paralysed. Nature. 1999;398(6725):297-8.
Birbaumer N, Kubler A, Ghanayim N, et al. The thought translation device (TTD) for completely paralyzed patients. IEEE Trans Rehabil Eng. 2000;8(2):190-3.
Monti MM, Vanhaudenhuyse A, Coleman MR, et al. Willful modulation of brain activity in disorders of consciousness. N Engl J Med. 2010;362:579-89.
Naito M, Michioka Y, Ozawa K, et al. A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE Trans Inf Syst. 2007;E90-D(7):1028-37.
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, Bakay RA, Moore MM, et al. Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng. 2000;8(2):198-202.
Brumberg JS, Nieto-Castanon A, Kennedy PR, Guenther FH. Brain-computer interfaces for speech communication. Speech Commun. 2010;52(4):367-79.
Hinterberger T, Widman G, Lal TN, et al. Voluntary brain regulation and communication with electrocorticogram signals. Epilepsy Behav. 2008;13(2):300-6.
Leuthardt EC, Schalk G, Wolpaw JR, et al. A brain-computer interface using electrocorticographic signals in humans. J Neural Eng. 2004;1(2):63-71.
Hill NJ, Lal TN, Schroder M, et al. Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):183-6.
Blankertz B, Sannelli C, Halder S, et al. Neurophysiological predictor of SMR-based BCI performance. Neuroimage. 2010;51(4):1303-9.
Giacino J, Ashwal S, Childs N, et al. The minimally conscious state: definition and diagnostic criteria. Neurology. 2002;58(3):349-53.