Brain–Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis

1. Birbeck, GL, Hanna, MG, Griggs, RC. Global opportunities and challenges for clinical neuroscience. JAMA. 2014;311(16):1609. doi:10.1001/jama.2014.2744.
Google Scholar | Crossref | Medline2. Kwakkel, G, Veerbeek, JM, van Wegen, EEH, Wolf, SL. Constraint-induced movement therapy after stroke. Lancet Neurol. 2015;14(2):224-234. doi:10.1016/S1474-4422(14)70160-7.
Google Scholar | Crossref | Medline3. Masiero, S, Armani, M, Ferlini, G, Rosati, G, Rossi, A. Randomized yrial of a robotic assistive device for the upper extremity during early inpatient stroke rehabilitation. Neurorehabil Neural Repair. 2014;28(4):377-386. doi:10.1177/1545968313513073.
Google Scholar | SAGE Journals | ISI4. Howlett, OA, Lannin, NA, Ada, L, McKinstry, C. Functional electrical stimulation improves activity after stroke: a systematic review with meta-analysis. Arch Phys Med Rehabil. 2015;96(5):934-943. doi:10.1016/j.apmr.2015.01.013.
Google Scholar | Crossref | Medline5. Shibata, K, Watanabe, T, Sasaki, Y, Kawato, M. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science. 2011;334(6061):1413-1415. doi:10.1126/science.1212003.
Google Scholar | Crossref | Medline | ISI6. Daly, JJ, Cheng, R, Rogers, J, Litinas, K, Hrovat, K, Dohring, M. Feasibility of a new application of noninvasive Brain Computer Interface (BCI): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther. 2009;33(4):203-211. doi:10.1097/NPT.0b013e3181c1fc0b.
Google Scholar | Crossref | Medline | ISI7. Cervera, MA, Soekadar, SR, Ushiba, J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol. May 2018;5(5):651-663. doi:10.1002/acn3.544.
Google Scholar | Crossref | Medline8. Bai, Z, Fong, KNK, Zhang, JJ, Chan, J, Ting, KH. Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis. J Neuroeng Rehabil. Apr 25 2020;17(1):57. doi:10.1186/s12984-020-00686-2.
Google Scholar | Crossref | Medline9. Coscia, M, Wessel, MJ, Chaudary, U, et al. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke. Brain. 2019;142(8):2182-2197. doi:10.1093/brain/awz181.
Google Scholar | Crossref | Medline10. Allred, RP, Kim, SY, Jones, TA. Use it and/or lose itâ€"experience effects on brain remodeling across time after stroke. Front Hum Neurosci. 2014;8:379. doi:10.3389/fnhum.2014.00379.
Google Scholar | Crossref | Medline11. Kai Keng Ang, KK, Cuntai Guan, C, Sui Geok Chua, K, et al. A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5981-5984. doi:10.1109/IEMBS.2009.5335381.
Google Scholar | Crossref | Medline12. Buetefisch, CM . Role of the contralesional hemisphere in post-stroke recovery of upper extremity motor function. Front Neurol. 2015;6:214. doi:10.3389/fneur.2015.00214.
Google Scholar | Crossref | Medline13. Soekadar, SR, Witkowski, M, Birbaumer, N, Cohen, LG. Enhancing hebbian learning to control brain oscillatory activity. Cerebr Cortex. 2015;25(9):2409-2415. doi:10.1093/cercor/bhu043.
Google Scholar | Crossref | Medline14. Takemi, M, Masakado, Y, Liu, M, Ushiba, J. Event-related desynchronization reflects downregulation of intracortical inhibition in human primary motor cortex. J Neurophysiol. 2013;110(5):1158-1166. doi:10.1152/jn.01092.2012.
Google Scholar | Crossref | Medline15. Jang, YY, Kim, TH, Lee, BH. Effects of brain-computer interface-controlled functional electrical stimulation training on shoulder subluxation for patients with stroke: a randomized controlled trial. Occup Ther Int. 2016;23(2):175-185. doi:10.1002/oti.1422.
Google Scholar | Crossref | Medline16. Platz, T, Kim, IH, Pintschovius, H, et al. Multimodal EEG analysis in man suggests impairment-specific changes in movement-related electric brain activity after stroke. Brain. 2000;123:2475-2490. doi:10.1093/brain/123.12.2475.
Google Scholar | Crossref | Medline17. Serrien, DJ, Strens, LHA, Cassidy, MJ, Thompson, AJ, Brown, P. Functional significance of the ipsilateral hemisphere during movement of the affected hand after stroke. Exp Neurol. 2004;190(2):425-432. doi:10.1016/j.expneurol.2004.08.004.
Google Scholar | Crossref | Medline | ISI18. Ushiba, J, Soekadar, SR. Brain-machine interfaces for rehabilitation of poststroke hemiplegia. Prog Brain Res. 2016;228:163-183. doi:10.1016/bs.pbr.2016.04.020.
Google Scholar | Crossref | Medline19. Mukaino, M, Ono, T, Shindo, K, et al. Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. J Rehabil Med. 2014;46(4):378-382. doi:10.2340/16501977-1785.
Google Scholar | Crossref | Medline20. Soekadar, SR, Witkowski, M, Vitiello, N, Birbaumer, N. An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand. Biomed Eng/Biomed Tech. 2015;60(3):199-205. doi:10.1515/bmt-2014-0126.
Google Scholar | Crossref | Medline21. Young, BM, Nigogosyan, Z, Walton, LM, et al. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng. 2014;7:26. doi:10.3389/fneng.2014.00026.
Google Scholar | Crossref | Medline22. López-Larraz, E, Sarasola-Sanz, A, Irastorza-Landa, N, Birbaumer, N, Ramos-Murguialday, A. Brain-machine interfaces for rehabilitation in stroke: A review. NeuroRehabilitation. 2018;43(1):77-97. doi:10.3233/NRE-172394.
Google Scholar | Crossref | Medline23. Mane, R, Chouhan, T, Guan, C. BCI for stroke rehabilitation: motor and beyond. J Neural Eng. 2020;17(4):041001. doi:10.1088/1741-2552/aba162.
Google Scholar | Crossref | Medline24. Moher, D, Liberati, A, Tetzlaff, J, Altman, DG, Group, P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535. doi:10.1136/bmj.b2535.
Google Scholar | Crossref | Medline25. Shamseer, L, Moher, D, Clarke, M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349:g7647. doi:10.1136/bmj.g7647.
Google Scholar | Crossref | Medline26. Higgins, JPT . Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557-560. doi:10.1136/bmj.327.7414.557.
Google Scholar | Crossref | Medline27. DerSimonian, R, Laird, N. Meta-analysis in clinical trials. Contr Clin Trials. 1986;7(3):177-188. doi:10.1016/0197-2456(86)90046-2.
Google Scholar | Crossref | Medline28. Egger, M, Smith, GD, Schneider, M, Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-634. doi:10.1136/bmj.315.7109.629.
Google Scholar | Crossref | Medline29. Ramos-Murguialday, A, Broetz, D, Rea, M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol. 2013;74(1):100-108. doi:10.1002/ana.23879.
Google Scholar | Crossref | Medline | ISI30. Ramos-Murguialday, A, Curado, MR, Broetz, D, Yilmaz, Ö, Brasil, FL, Liberati, G, et al. Brain-machine interface in chronic stroke: randomized trial long-term follow-up. Neurorehabil Neural Repair. 2019;33(3):188-198. doi:10.1177/1545968319827573.
Google Scholar | SAGE Journals | ISI31. Ang, KK, Chua, KSG, Phua, KS, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci. 2015;46(4):310-320. doi:10.1177/1550059414522229.
Google Scholar | SAGE Journals | ISI32. Ang, KK, Guan, C, Phua, KS, et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng. 2014;7. doi:10.3389/fneng.2014.00030.
Google Scholar | Crossref | Medline33. Biasiucci, A, Leeb, R, Iturrate, I, Perdikis, S, Al-Khodairy, A, Corbet, T, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun. 2018;9(1). doi:10.1038/s41467-018-04673-z.
Google Scholar | Crossref | Medline34. Frolov, AA, Mokienko, O, Lyukmanov, R, et al. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: a randomized controlled multicenter trial. Front Neurosci. 2017;11. doi:10.3389/fnins.2017.00400.
Google Scholar | Crossref | Medline35. Kim, T, Kim, S, Lee, B. Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial. Occup Ther Int. 2016;23(1):39-47. doi:10.1002/oti.1403.
Google Scholar | Crossref | Medline36. Li, M, Liu, Y, Wu, Y, Liu, S, Jia, J, Zhang, L. Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training. Int J Neurosci. 2014;124(6):403-415. doi:10.3109/00207454.2013.850082.
Google Scholar | Crossref | Medline37. Mihara, M, Hattori, N, Hatakenaka, M, et al. Near-infrared Spectroscopy-mediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims. Stroke. 2013;44(4):1091-1098. doi:10.1161/strokeaha.111.674507.
Google Scholar | Crossref | Medline | ISI38. Pichiorri, F, Morone, G, Petti, M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015;77(5):851-865. doi:10.1002/ana.24390.
Google Scholar | Crossref | Medline39. Curado, MR, Cossio, EG, Broetz, D, et al. Residual upper arm motor function primes innervation of paretic forearm muscles in chronic stroke after brain-machine interface (BMI) training. PLoS One. 2015;10(10):e0140161. doi:10.1371/journal.pone.0140161.
Google Scholar | Crossref | Medline40. Rayegani, SM, Raeissadat, SA, Sedighipour, L, Mohammad Rezazadeh, I, Bahrami, MH, Eliaspour, D, et al. Effect of neurofeedback and electromyographic-biofeedback therapy on improving hand function in stroke patients. Top Stroke Rehabil. 2014;21(2):137-151. doi:10.1310/tsr2102-137.
Google Scholar | Crossref | Medline41. Várkuti, B, Guan, C, Pan, Y, et al. Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair. 2013;27(1):53-62. doi:10.1177/1545968312445910.
Google Scholar | SAGE Journals | ISI42. Cheng, N, Phua, KS, Lai, HS, et al. Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE (Inst Electr Electron Eng) Trans Biomed Eng. 2020;67(12):3339-3351. doi:10.1109/TBME.2020.2984003.
Google Scholar | Crossref | Medline43. Mottaz, A, Corbet, T, Doganci, N, et al. Modulating functional connectivity after stroke with neurofeedback: effect on motor deficits in a controlled cross-over study. Neuroimage: Clinic. 2018;20:336-346. doi:10.1016/j.nicl.2018.07.029.
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