AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
View PDF
Submit Manuscript AI Chat Paper
Show Outline
Show full outline
Hide outline
Show full outline
Hide outline
Review Article | Open Access

State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review

Miaomiao Zhuang1,5,§Qingheng Wu2,5,§Feng Wan3Yong Hu4,5( )
Xiangya School of Medicine, Central South University, Changsha 410013, Hunan, China;
Department of Dentistry, Nanjing Medical University, Nanjing 211166, Jiangsu, China;
Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China;
Shenzhen Key Laboratory for Innovative Technology in Orthopaedic Trauma, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China;
Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, China

§ These authors contributed equally to this work.

Show Author Information


Brain–computer interface (BCI) is a novel communication method between brain and machine. It enables signals from the human brain to influence or control external devices. Currently, much research interest is focused on the BCI-based neural rehabilitation of patients with motor and cognitive diseases. Over the decades, BCI has become an alternative treatment for motor and cognitive rehabilitation. Previous studies demonstrated the usefulness of BCI intervention in restoring motor function and recovery of the damaged brain. Electroencephalogram (EEG)-based BCI intervention could cast light on the mechanisms underlying neuroplasticity during upper limb recovery by providing feedback to the damaged brain. BCI could act as a useful tool to aid patients with daily communication and basic movement in severe motor loss cases like amyotrophic lateral sclerosis (ALS). Furthermore, recent findings have reported the therapeutic efficacy of BCI in people suffering from other diseases with different levels of motor impairment such as spastic cerebral palsy, neuropathic pain, etc. Besides motor functional recovery, BCI also plays its role in improving the behavior of patients with cognitive diseases like attention-deficit/hyperactivity disorder (ADHD). The BCI-based neurofeedback training is focused on either reducing the ratio of theta and beta rhythm, or enabling the patients to regulate their own slow cortical potentials, and both have made progress in increasing attention and alertness. With summary of several clinical studies with strong evidence, we present cutting edge results from the clinical application of BCI in motor and cognitive diseases, including stroke, spinal cord injury, ALS, and ADHD.


JJ Shih, DJ Krusienski, JR Wolpaw. Brain-computer interfaces in medicine. Mayo Clin Proc. 2012, 87(3): 268-279.
JS Brumberg, A Nieto-Castanon, PR Kennedy, et al. Brain-computer interfaces for speech communication. Speech Commun. 2010, 52(4): 367-379.
LR Hochberg, D Bacher, B Jarosiewicz, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 2012, 485(7398): 372-375.
R Bertani, C Melegari, MC De Cola, et al. Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurol Sci. 2017, 38(9): 1561-1569.
X Qian, BRY Loo, FX Castellanos, et al. Brain- computer-interface-based intervention re-normalizes brain functional network topology in children with attention deficit/hyperactivity disorder. Transl Psychiatry. 2018, 8(1): 149.
EJ Benjamin, MJ Blaha, SE Chiuve, et al. Heart disease and stroke statistics-2017 update: A report from the American Heart Association. Circulation. 2017, 135(10): e146-e603.
A Curt, HJ van Hedel, D Klaus, et al. Recovery from a spinal cord injury: significance of compensation, neural plasticity, and repair. J Neurotrauma. 2008, 25(6): 677-685.
F Pichiorri, G Morone, M Petti, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015, 77(5): 851-865.
CG Lim, XWW Poh, SSD Fung, et al. A randomized controlled trial of a brain-computer interface based attention training program for ADHD. PLoS One. 2019, 14(5): e0216225.
J Takahashi, A Yasumura, E Nakagawa, et al. Changes in negative and positive EEG shifts during slow cortical potential training in children with attention- deficit/hyperactivity disorder: a preliminary investigation. Neuroreport. 2014, 25(8): 618-624.
N Yoshida, Y Hashimoto, M Shikota, et al. Relief of neuropathic pain after spinal cord injury by brain- computer interface training. Spinal Cord Ser Cases. 2016, 2: 16021.
EV Friedrich, N Suttie, A Sivanathan, et al. Brain- computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum. Front Neuroeng. 2014, 7: 21.
CG Lim, TS Lee, C Guan, et al. A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS One. 2012, 7(10): e46692.
B Hillard, AS El-Baz, L Sears, et al. Neurofeedback training aimed to improve focused attention and alertness in children with ADHD: a study of relative power of EEG rhythms using custom-made software application. Clin EEG Neurosci. 2013, 44(3): 193-202.
AR Bakhshayesh, S Hänsch, A Wyschkon, et al. Neurofeedback in ADHD: a single-blind randomized controlled trial. Eur Child Adolesc Psychiatry. 2011, 20(9): 481-491.
U Strehl, U Leins, G Goth, et al. Self-regulation of slow cortical potentials: a new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics. 2006, 118(5): e1530-e1540.
F Lotte, L Bougrain, A Cichocki, et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng. 2018, 15(3): 031005.
A Bashashati, M Fatourechi, RK Ward, et al. A survey of signal processing algorithms in brain- computer interfaces based on electrical brain signals. J Neural Eng. 2007, 4(2): R32-R57.
H Ramoser, J Müller-Gerking, G Pfurtscheller. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 2000, 8(4): 441-446.
G Pfurtscheller, C Neuper. Motor imagery and direct brain-computer communication. Proc IEEE. 2001, 89(7): 1123-1134.
F Lotte, M Congedo, A Lécuyer, et al. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng. 2007, 4(2): R1-R13.
KR Müller, CW Anderson, GE Birch. Linear and nonlinear methods for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2003, 11(2): 165-169.
R Rupp, A Kreilinger, M Rohm, et al. Development of a non-invasive, multifunctional grasp neuroprosthesis and its evaluation in an individual with a high spinal cord injury. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, USA, 2012, pp 1835–1838.
JR Wolpaw, N Birbaumer, DJ McFarland, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002, 113(6): 767-791.
DJ McFarland, J Daly, C Boulay, et al. Therapeutic applications of BCI technologies. Brain Comput Interfaces (Abingdon). 2017, 47(1/2): 37-52.
R Béné, N Beck, B Vajda, et al. Interface providers in stroke neurorehabilitation. Period Biol, 2012, 114(3): 403-407.
J Song, BM Young, Z Nigogosyan, et al. Characterizing relationships of DTI, fMRI, and motor recovery in stroke rehabilitation utilizing brain-computer interface technology. Front Neuroeng. 2014, 7: 31.
J Song, VA Nair, BM Young, et al. DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology. Front Hum Neurosci. 2015, 9: 195.
A Muralidharan, J Chae, DM Taylor. Extracting attempted hand movements from EEGs in people with complete hand paralysis following stroke. Front Neurosci. 2011, 5: 39.
JE Huggins, C Guger, M Ziat, et al. Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future. Brain-Comput Interfaces. 2017, 4(1/2): 3-36.
N Mrachacz-Kersting, N Jiang, AJ Stevenson, et al. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J Neurophysiol. 2016, 115(3): 1410-1421.
T Nierhaus, C Vidaurre, C Sannelli, et al. Immediate brain plasticity after one hour of brain-computer interface (BCI). J Physiol. 2019, in press, .
JH Crosbie, SM McDonough, DH Gilmore, et al. The adjunctive role of mental practice in the rehabilitation of the upper limb after hemiplegic stroke: a pilot study. Clin Rehabil. 2004, 18(1): 60-68.
RI Carino-Escobar, P Carrillo-Mora, R Valdés-Cristerna, et al. Longitudinal analysis of stroke patients’ brain rhythms during an intervention with a brain-computer interface. Neural Plast. 2019, 2019: 7084618.
A Vourvopoulos, OM Pardo, S Lefebvre, et al. Effects of a brain-computer interface with virtual reality (VR) neurofeedback: a pilot study in chronic stroke patients. Front Hum Neurosci. 2019, 13: 210.
J Toppi, D Mattia, A Anzolin, et al. Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment. Conf Proc IEEE Eng Med Biol Soc. 2014, 2014: 6786-6789.
ES Lawrence, C Coshall, R Dundas, et al. Estimates of the prevalence of acute stroke impairments and disability in a multiethnic population. Stroke. 2001, 32(6): 1279-1284.
CE Lang, JA Beebe. Relating movement control at 9 upper extremity segments to loss of hand function in people with chronic hemiparesis. Neurorehabil Neural Repair. 2007, 21(3): 279-291.
P Langhorne, F Coupar, A Pollock. Motor recovery after stroke: a systematic review. Lancet Neurol. 2009, 8(8): 741-754.
RJ Marino, JF Ditunno Jr, WH Donovan, et al. Neurologic recovery after traumatic spinal cord injury: data from the Model Spinal Cord Injury Systems. Arch Phys Med Rehabil. 1999, 80(11): 1391-1396.
AH Do, PT Wang, CE King, et al. Brain-computer interface controlled robotic gait orthosis. J Neuroeng Rehabil. 2013, 10: 111.
JL Collinger, B Wodlinger, JE Downey, et al. High- performance neuroprosthetic control by an individual with tetraplegia. Lancet. 2013, 381(9866): 557-564.
T Aflalo, S Kellis, C Klaes, et al. Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science. 2015, 348(6237): 906-910.
AB Remsik, K Dodd, L Williams Jr, et al. Behavioral outcomes following brain-computer interface intervention for upper extremity rehabilitation in stroke: a randomized controlled trial. Front Neurosci. 2018, 12: 752.
E Guanziroli, M Cazzaniga, L Colombo, et al. Assistive powered exoskeleton for complete spinal cord injury: correlations between walking ability and exoskeleton control. Eur J Phys Rehabil Med. 2019, 55(2): 209-216.
DR Louie, JJ Eng, T Lam, et al. Gait speed using powered robotic exoskeletons after spinal cord injury: a systematic review and correlational study. J Neuroeng Rehabil. 2015, 12: 82.
JL Contreras-Vidal, N A Bhagat, J Brantley, et al. Powered exoskeletons for bipedal locomotion after spinal cord injury. J Neural Eng. 2016, 13(3): 031001.
A Kilicarslan, S Prasad, RG Grossman, et al. High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton. Conf Proc IEEE Eng Med Biol Soc. 2013, 2013: 5606-5609.
M Capogrosso, T Milekovic, D Borton, et al. A brain–spine interface alleviating gait deficits after spinal cord injury in Primates. Nature. 2016, 539(7628): 284-288.
TD Aumann, Y Prut. Do sensorimotor β-oscillations maintain muscle synergy representations in primary motor cortex? Trends Neurosci. 2015, 38(2): 77-85.
TW Kim, BH Lee. Clinical usefulness of brain- computer interface-controlled functional electrical stimulation for improving brain activity in children with spastic cerebral palsy: a pilot randomized controlled trial. J Phys Ther Sci. 2016, 28(9): 2491-2494.
JR Wolpaw, RS Bedlack, DJ Reda, et al. Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis. Neurology. 2018, 91(3): e258-e267.
L Carelli, F Solca, A Faini, et al. Brain-computer interface for clinical purposes: cognitive assessment and rehabilitation. Biomed Res Int. 2017, 2017: 1695290.
G Santhanam, SI Ryu, BM Yu, et al. A high- performance brain-computer interface. Nature. 2006, 442(7099): 195-198.
ADHD: clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/ hyperactivity disorder in children and adolescents. Pediatrics. 2011, 128(5): 1007-1022.
M Arns, S de Ridder, U Strehl, et al. Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta- analysis. Clin EEG Neurosci. 2009, 40(3): 180-189.
J van Doren, M Arns, H Heinrich, et al. Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis. Eur Child Adolesc Psychiatry. 2019, 28(3): 293-305.
MA Vollebregt, M van Dongen-Boomsma, JK Buitelaar, et al. Does EEG-neurofeedback improve neurocognitive functioning in children with attention- deficit/hyperactivity disorder? A systematic review and a double-blind placebo-controlled study. J Child Psychol Psychiatry. 2014, 55(5): 460-472.
M Arns, H Heinrich, U Strehl. Evaluation of neurofeedback in ADHD: the long and winding road. Biol Psychol. 2014, 95: 108-115.
SM Snyder, JR Hall. A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. J Clin Neurophysiol. 2006, 23(5): 440-455.
P Coutin-Churchman, Y Añez, M Uzcátegui, et al. Quantitative spectral analysis of EEG in psychiatry revisited: drawing signs out of numbers in a clinical setting. Clin Neurophysiol. 2003, 114(12): 2294-2306.
JJ Daly, JR Wolpaw. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008, 7(11): 1032-1043.
M Grosse-Wentrup, D Mattia, K Oweiss. Using brain-computer interfaces to induce neural plasticity and restore function. J Neural Eng. 2011, 8(2): 025004.
T Wieloch, K Nikolich. Mechanisms of neural plasticity following brain injury. Curr Opin Neurobiol. 2006, 16(3): 258-264.
NS Ward. Neural plasticity and recovery of function. In Progress in Brain Research. Amsterdam: Elsevier, 2005.
E Buch, C Weber, LG Cohen, et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. 2008, 39(3): 910-917.
SR Soekadar, N Birbaumer, MW Slutzky, et al. Brain-machine interfaces in neurorehabilitation of stroke. Neurobiol Dis. 2015, 83: 172-179.
CE King, PT Wang, LA Chui, et al. Operation of a brain-computer interface walking simulator for individuals with spinal cord injury. J Neuroeng Rehabil. 2013, 10: 77.
FD Broccard, T Mullen, YM Chi, et al. Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders. Ann Biomed Eng. 2014, 42(8): 1573-1593.
F Nijboer, A Furdea, I Gunst, et al. An auditory brain-computer interface (BCI). J Neurosci Methods. 2008, 167(1): 43-50.
T Cao, F Wan, CM Wong, et al. Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces. Biomed Eng Online. 2014, 13(1): 28.
A Chatterjee, V Aggarwal, A Ramos, et al. A brain- computer interface with vibrotactile biofeedback for haptic information. J Neuroeng Rehabil. 2007, 4: 40.
DW Tan, MA Schiefer, MW Keith, et al. A neural interface provides long-term stable natural touch perception. Sci Transl Med. 2014, 6(257): 257ra138.
S Santaniello, G Fiengo, L Glielmo, et al. Closed- loop control of deep brain stimulation: a simulation study. IEEE Trans Neural Syst Rehabil Eng. 2011, 19(1): 15-24.
E Fernández, B Greger, PA House, et al. Acute human brain responses to intracortical microelectrode arrays: challenges and future prospects. Front Neuroeng. 2014, 7: 24.
CM Stinear. Stroke rehabilitation research needs to be different to make a difference. F1000Res. 2016, 5: F1000 Faculty Rev–1467.
Journal of Neurorestoratology
Pages 12-25
Cite this article:
Zhuang M, Wu Q, Wan F, et al. State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review. Journal of Neurorestoratology, 2020, 8(1): 12-25.








Web of Science




Received: 09 October 2019
Revised: 02 February 2020
Accepted: 05 February 2020
Published: 05 March 2020
© The authors 2020

This article is published with open access at