Application of BCI systems in neurorehabilitation: a scoping review

References

• Allison BZ, Wolpaw EW, Wolpaw JR. Brain–computer interface systems: progress and prospects. Exp Rev Med Dev 2007;4:463–74
   PubMed Web of Science ®Google Scholar
• Levac D, Colquhoun H, O’Brien K. Scoping studies: advancing the methodology. Implement Sci 2010;5:69
   PubMed Web of Science ®Google Scholar
• Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Soc Res Meth 2005;8:19–3
   Google Scholar
• Graimann B, Allison B, Pfurtscheller G. Brain–computer interfaces: a gentle introduction. Heidelberg: Springer; 2010
   Google Scholar
• Shih JJ, Krusienski DJ, Wolpaw JR. Brain–computer interfaces in medicine. Mayo Clin Proc 2012;87:268–79
   PubMed Web of Science ®Google Scholar
• Wolpaw JR, Birbaumer N, Heetderks WJ, et al. Brain–computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 2000;8:164–73
   PubMedGoogle Scholar
• Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol 2002;113:767–91
   PubMed Web of Science ®Google Scholar
• Lebedev MA, Nicolelis MA. Brain? Machine interfaces: past, present and future. TRENDS Neurosci 2006;29:536–46
   PubMed Web of Science ®Google Scholar
• Sun H, Xiang Y, Yang M. Neurological rehabilitation of stroke patients via motor imaginary-based brain–computer interface technology. Clin Neurophysiol 2011;47:11–16
   Google Scholar
• Murguialday AR, Aggarwal V, Chatterjee A, et al. Brain–computer interface for a prosthetic hand using local machine control and haptic feedback. Rehabilitation Engineering IEEE 10th International Conference; 2007 June 13–15; Noordwijk, 609–13
   Google Scholar
• Wolpaw JR, Loeb GE, Allison BZ, et al. BCI meeting 2005-workshop on signals and recording methods. Neural Syst Rehabil Eng 2006;14:138–41
   PubMed Web of Science ®Google Scholar
• Dornhege G. Toward brain–computer interfacing. Cambridge, MA: The MIT Press; 2007
   Google Scholar
• Niedermeyer E, Da Silva FL. Electroencephalography: basic principles, clinical applications, and related fields. Philadelphia: Lippincott Williams & Wilkins; 2005
   Google Scholar
• Pfurtscheller G, Neuper C, Schlogl A, et al. Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. Rehabil Eng 1998;6:316–25
   PubMedGoogle Scholar
• Picton T. Human brain electrophysiology. Evoked potentials and evoked magnetic fields in science and medicine. Clin Neurophysiol 1990;7:450–1
   Google Scholar
• Wang Y, Wang R, Gao X, et al. A practical VEP-based brain–computer interface. Neural Syst Rehabil Eng 2006;14:234–40
   PubMed Web of Science ®Google Scholar
• Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. Biomed Eng 2006;53:2610–14
   Google Scholar
• Birbaumer N, Elbert T, Canavan AG, et al. Slow potentials of the cerebral cortex and behavior. Physiol Rev 1990;70:1–41
   PubMed Web of Science ®Google Scholar
• Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 1988;70:510–23
   PubMedGoogle Scholar
• Donchin E, Smith DBD. The contingent negative variation and the late positive wave of the average evoked potential. Electroencephalogr Clin Neurophysiol 1970;29:201–3
   PubMedGoogle Scholar
• Ravden D, Polich J. On P300 measurement stability: habituation, intra-trial block variation, and ultradian rhythms. Biol Psychol 1999;51:59–76
   PubMed Web of Science ®Google Scholar
• Jeannerod M. Mental imagery in the motor context. Neuropsychologia 1995;33:1419–32
   PubMed Web of Science ®Google Scholar
• Pfurtscheller G, Neuper C. Motor imagery and direct brain–computer communication. Proc IEEE 2001;89:1123–34
   Web of Science ®Google Scholar
• Smith RC. Electroencephalograph based brain computer interfaces [Doctoral dissertation]. Dublin, University College Dublin (NUI); 2004:159–67
   Google Scholar
• Guger C, Schlogl A, Neuper C, et al. Rapid prototyping of an EEG-based brain–computer interface (BCI). Neural Syst Rehabil Eng 2011;9:49–58
   Google Scholar
• Curran EA, Stokes MJ. Learning to control brain activity: a review of the production and control of EEG components for driving brain–computer interface (BCI) systems. Brain Cogn 2003;51:326–36
   PubMed Web of Science ®Google Scholar
• Pasqualotto E, Federici S, Belardinelli MO. Toward functioning and usable brain–computer interfaces (BCIs): a literature review. Disabil Rehabil Assist Technol 2012;7:89–103
   PubMedGoogle Scholar
• Birbaumer N. Breaking the silence: brain–computer interfaces (BCI) for communication and motor control. Psychophysiology 2006;43:517–32
   PubMed Web of Science ®Google Scholar
• Moore MM. Real-world applications for brain–computer interface technology. Neural Syst Rehabil Eng 2003;11:162–5
   PubMed Web of Science ®Google Scholar
• Bashashati A, Fatourechi M, Ward RK, et al. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. Neural Eng 2007;4:1–32
   PubMedGoogle Scholar
• Iturrate I, Antelis JM, Kubler A, et al. A non-invasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. Robotics 2009;25:614–27
   Google Scholar
• Leishman F, Horn O, Bourhis G. Smart wheelchair control through a deictic approach. Robot Auton Syst 2010;58:1149–58
   Google Scholar
• Galán F, Nuttin M, Lew E, et al. A brain-actuated wheelchair: asynchronous and non-invasive brain–computer interfaces for continuous control of robots. Clin Neurophysiol 2008;119:2159–69
   PubMed Web of Science ®Google Scholar
• Wang H, Song Q, Zhang L, et al. Design on the control system of a gait rehabilitation training robot based on brain-computer interface and virtual reality technology. Adv Robot Syst 2012;9:121–8
   Google Scholar
• Luth T, Ojdanic D, Friman O, et al. Low level control in a semi-autonomous rehabilitation robotic system via a brain–computer interface. Rehabil Robot 2007;10:721–8
   Google Scholar
• Khare V, Santhosh J, Anand S, et al. Brain computer interface based real time control of wheelchair using electroencephalogram. Soft Computing 2003;1:13–19
   Google Scholar
• King CE, Wang PT, Mizuta M, et al. Noninvasive brain–computer interface driven hand orthosis. Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE; 2011:5786–9; Boston, MA
   Google Scholar
• Guger C, Harkam W, Hertnaes C, et al. Prosthetic control by an EEG-based brain–computer interface (BCI). Proceedings of the AAATE 5th European conference for the advancement of assistive technology. 1999;4:3–6 ; Dusseldorf, Germany
   Google Scholar
• Fok S, Schwartz R, Wronkiewicz M, et al. An EEG-based brain computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology. Conf Proc IEEE Eng Med Biol Soc 2011;2011:6277–80
   Google Scholar
• Zhou J, Yao J, Deng J, et al. EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects. Comput Biol Med 2009;39:443–50
   PubMedGoogle Scholar
• Chiou JC, Liang SC, Yen CH, et al. EEG-controlled table bike for neurorehabilitation based on sensorimotor-rhythm BCI. COGNITIVE 2012. The Fourth International Conference on Advanced Cognitive Technologies and Applications; 2012:95–7; Nice, France
   Google Scholar
• Qian K, Nikolov P, Huang D, et al. A motor imagery-based online interactive brain-controlled switch: paradigm development and preliminary test. Clin Neurophysiol 2010;121:13–19
   Google Scholar
• Guger C, Holzner C, Groenegress C, et al. Brain–computer interface for virtual reality control. Proceedings of 17th European Symposium on Artificial Neural Networks; 2009:443–8; Bruges, Belgium
   Google Scholar
• De la Vega Arias J, Hintermüller C, Guger C. Generic brain–computer interface for social and human-computer interaction. ACHI 2012, The Fifth International Conference on Advances in Computer-Human Interactions; 2012 Jan 30; Valencia, Spain:145–9
   Google Scholar
• Hoffmann U, Vesin JM, Ebrahimi T, et al. An efficient P300-based brain–computer interface for disabled subjects. Neurosci Meth 2008;167:115–25
   PubMed Web of Science ®Google Scholar
• Sellers EW, Donchin E. A P300-based brain–computer interface: initial tests by ALS patients. Clin Neurophysiol 2006;117:538–48
   PubMed Web of Science ®Google Scholar
• Piccione F, Giorgi F, Tonin P, et al. P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin Neurophysiol 2006;117:531–7
   PubMed Web of Science ®Google Scholar
• Shishkin SL, Ganin IP, Kaplan AY. Event-related potentials in a moving matrix modification of the P300 brain–computer interface paradigm. Neurosci Lett 2011;496:95–9
   PubMed Web of Science ®Google Scholar
• Royer AS, Doud AJ, Rose ML, et al. EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. Neural Syst Rehabil Eng 2010;18:581–9
   PubMedGoogle Scholar
• Gentiletti GG, Gebhart JG, Acevedo RC, et al. Command of a simulated wheelchair on a virtual environment using a brain–computer interface. IRBM 2009;30:218–25
   Google Scholar
• Krepki R, Curio G, Blankertz B, et al. Berlin brain–computer interface – the HCI communication channel for discovery. Int Hum-Comput Studies 2007;65:460–77
   Google Scholar
• Blankertz B, Dornhege G, Krauledat M, et al. The non-invasive Berlin Brain–computer Interface: fast acquisition of effective performance in untrained subjects. NeuroImage 2007;37:539–50
   PubMed Web of Science ®Google Scholar
• Wu CH, Chang HC, Lee PL, et al. Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing. Neurosci Meth 2011;196:170–81
   PubMed Web of Science ®Google Scholar
• Lauer RT, Peckham PH, Kilgore KL. EEG-based control of a hand graspneuroprosthesis. Neuroreport 1999;10:1767–72
   PubMed Web of Science ®Google Scholar
• Cheng M, Jia W, Gao X, et al. Mu rhythm-based cursor control: an offline analysis. Clin Neurophysiol 2004;115:745–51
   PubMedGoogle Scholar
• Li Y, Long J, Yu T, et al. An EEG-based BCI system for 2-D cursor control by combining mu/beta rhythm and P300 potential. Biomed Eng 2010;57:2495–505
   Google Scholar
• Allison BZ, Brunner C, Altstätter C, et al. A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control. Neurosci Meth 2012;28:61–8
   Google Scholar
• Lee PL, Hsieh JC, Wu CH, et al. Brain computer interface using flash onset and offset visual evoked potentials. Clin Neurophysiol 2008;119:605–16
   PubMed Web of Science ®Google Scholar
• Hazrati MK, Erfanian A. An online EEG-based brain–computer interface for controlling hand grasp using an adaptive probabilistic neural network. Med Eng Phys 2010;32:730–9
   PubMedGoogle Scholar
• Ron-Angevin R, Díaz-Estrella A. Brain–computer interface: changes in performance using virtual reality techniques. Neurosci Lett 2009;449:118–23
   Google Scholar
• Liao LD, Chen CY, Wang IJ, et al. Gaming control using a wearable and wireless EEG-based brain–computer interface device with novel dry foam-based sensors. Neuroeng Rehabil 2012;9:5–12
   PubMed Web of Science ®Google Scholar
• Pérez-Marcos D, Buitrago JA, Velásquez FDG. Writing through a robot: a proof of concept for a brain–machine interface. Med Eng Phys 2011;33:1314–17
   PubMedGoogle Scholar
• Sellers EW, Krusienski DJ, McFarland DJ, et al. A P300 event-related potential brain–computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biol Psychol 2006;73:242–52
   PubMed Web of Science ®Google Scholar
• Münβinger JI, Halder S, Kleih SC, et al. Brain painting: first evaluation of a new brain–computer interface application with ALS-patients and healthy volunteers. Front Neurosci 2010;4:142–50
   Google Scholar
• Neshige R, Murayama N, Igasaki T, et al. Communication aid device utilizing event-related potentials for patients with severe motor impairment. Brain Res 2007;1141:218–27
   PubMed Web of Science ®Google Scholar
• Allison BZ, Pineda JA. Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system. Int Psychophysiol 2006;59:127–40
   PubMed Web of Science ®Google Scholar
• Kleih SC, Nijboer F, Halder S, et al. Motivation modulates the P300 amplitude during brain–computer interface use. Clin Neurophysiol 2010;121:1023–31
   PubMed Web of Science ®Google Scholar
• Kaufmann T, Vögele C, Sütterlin S, et al. Effects of resting heart rate variability on performance in the P300 brain–computer interface. Int Psychophysiol 2012;83:336–41
   PubMedGoogle Scholar
• Jin J, Allison BZ, Wang X, et al. A combined brain–computer interface based on P300 potentials and motion-onset visual evoked potentials. Neurosci Meth 2012;205:265–76
   PubMed Web of Science ®Google Scholar
• Takano K, Komatsu T, Hata N, et al. Visual stimuli for the P300 brain–computer interface: a comparison of white/gray and green/blue flicker matrices. Clin Neurophysiol 2009;120:1562–6
   PubMed Web of Science ®Google Scholar
• Li Y, Guan C, Li H., et al. A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system. Pattern Recog Lett 2008;29:1285–94
   Web of Science ®Google Scholar
• Sirvent Blasco JL, Iáñez E, Úbeda A, et al. Visual evoked potential-based brain–machine interface applications to assist disabled people. Expert Syst Appl 2012;39:7908–18
   Google Scholar
• Hwang HJ, Lim JH, Jung YJ, et al. Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard. Neurosci Meth 2012;17:51–7
   Google Scholar
• Cheng M, Gao X, Gao S, et al. Design and implementation of a brain–computer interface with high transfer rates. Biomed Eng 2002;49:1181–6
   Google Scholar
• Scherer R, Muller GR, Neuper C, et al. An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. Biomed Eng 2004;51:979–84
   Google Scholar
• Birbaumer N, Kubler A, Ghanayim N, et al. The thought translation device (TTD) for completely paralyzed patients. Rehabil Eng 2000;8:190–3
   PubMedGoogle Scholar
• Klobassa DS, Vaughan TM, Brunner P, et al. Toward a high-throughput auditory P300-based brain–computer interface. Clin Neurophysiol 2009;120:1252–9
   PubMed Web of Science ®Google Scholar
• Kübler 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
   PubMed Web of Science ®Google Scholar
• Ferrez P, Millán J. EEG-based brain–computer interaction: improved accuracy by automatic single-trial error detection. 21st Annual Conference on Neural Information Processing Systems 2007:441–8 ; Vancouver, Canada
   Google Scholar
• Hoang T, Nguyen P, Le T, et al. Enhancing performance of SVM-based brain–computer interface. Intelligent Inform Process Syst 2010;11:19–26
   Google Scholar
• Fazli S, Mehnert J, Steinbrink J, et al. Enhanced performance by a hybrid NIRS–EEG brain computer interface. Neuroimage 2012;59:519–29
   PubMed Web of Science ®Google Scholar
• Lin PT, Sharma K, Holroyd T, et al. A high performance MEG based BCI using single trial detection of human movement intention. Neuroscience, Chapter 2, INTECH (open access); 2013:17–36
   Google Scholar
• Wriessnegger S, Scherer R, Mörth K, et al. Heart rate-controlled EEG-based BCI: the Graz hybrid BCI. Proceedings of the 3rd International BCI Workshop & Training Course; 2006, Graz, Austria, Graz University of Technology Publishing House
   Google Scholar
• Jerbi K, Vidal JR, Mattout J, et al. Inferring hand movement kinematics from MEG, EEG and intracranial EEG: From brain-machine interfaces to motor rehabilitation. IRBM 2011;32:8–18
   Google Scholar
• Zander TO, Kothe C, Jatzev S, Gaertner M. Enhancing human–computer interaction with input from active and passive brain–computer interfaces. Brain–computer interfaces. London: Springer; 2010:181–99
   Google Scholar
• Halder S, Rea M, Andreoni R, et al. An auditory oddball brain–computer interface for binary choices. Clin Neurophysiol 2010;121:516–23
   PubMed Web of Science ®Google Scholar
• Nijboer F, Furdea A, Gunst I, et al. An auditory brain–computer interface (BCI). Neurosci Meth 2008;167:43–50
   PubMed Web of Science ®Google Scholar
• Rutkowski TM, Vialatte F, Cichocki A, et al. Auditory feedback for brain computer interface management–an EEG data sonification approach. Knowledge-based intelligent information and engineering systems. Berlin: Springer; 2006:1232–9
   Google Scholar
• Brumberg JS, Nieto-Castanon A, Kennedy PR, et al. Brain–computer interfaces for speech communication. Speech Commun 2010;52:367–79
   PubMed Web of Science ®Google Scholar
• Dobkin BH. Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. Physiol 2007;579:637–42
   Google Scholar
• Aloise F, Lasorsa I, Schettini F, et al. Multimodal stimulation for a P300-based BCI. Bioelectromagn 2007;9:128–30
   Google Scholar
• Neshige R, Endou T, Miyamoto T, et al. Proposal of P300 biofeedback therapy in patients with mental disturbances as cognitive rehabilitation. Jpn J Rehabil Med 1995;32:323–9
   Google Scholar
• Mori H, Matsumoto Y, Kryssanov V, et al. Multi-command tactile brain computer interface: a feasibility study. Haptic and audio interaction design. Heidelberg: Springer; 2013:50–9
   Google Scholar
• Buch E, Weber C, Cohen LG, et al. Think to move: a neuromagnetic brain-compute interface (BCI) system for chronic stroke. Stroke 2008;39:910–17
   PubMed Web of Science ®Google Scholar
• Kübler A, Birbaumer N. Brain–computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin Neurophysiol 2008;119:2658–63
   PubMed Web of Science ®Google Scholar
• Jeon Y, Nam CS, Kim YJ, et al. Event-related (De) synchronization (ERD/ERS) during motor imagery tasks: implications for brain–computer interfaces. Int Indus Ergonomics 2011;41:428–36
   Google Scholar
• Bai O, Lin P, Huang D, et al. Towards a user-friendly brain–computer interface: initial tests in ALS and PLS patients. Clinl Neurophysiol: Neurophysiol 2010;121:1293–302
   PubMed Web of Science ®Google Scholar
• Yue J, Zhou Z, Jiang J, et al. Balancing a simulated inverted pendulum through motor imagery: an EEG-based real-time control paradigm. Neurosci Lett 2012;123:13–21
   Google Scholar
• Pires G, Nunes U, Castelo-Branco M. Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients. Clin Neurophysiol 2012;123:1168–74
   PubMed Web of Science ®Google Scholar
• Xu Q, Zhou H, Wang Y, et al. Fuzzy support vector machine for classification of EEG signals using wavelet-based features. Med Eng Phys 2009;31:858–64
   PubMedGoogle Scholar
• Mousavi EA, Maller JJ, Fitzgerald PB, et al. Wavelet common spatial pattern in asynchronous offline brain computer interfaces. Biomed Signal Process Control 2011;6:121–8
   Google Scholar
• Iscan Z, Dokur Z, Demiralp T. Classification of electroencephalogram signals with combined time and frequency features. Expert Syst Appl 2011;38:10499–505
   Web of Science ®Google Scholar
• Blankertz B, Dornhege G, Krauledat M, et al. The Berlin brain-computer interface: EEG-based communication without subject training. IEEE Trans Neural Syst Rehabil Eng 2006;14:147–52
   PubMed Web of Science ®Google Scholar
• Townsend G, LaPallo BK, Boulay CB, et al. A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 2010;121:1109–15
   PubMed Web of Science ®Google Scholar