A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke

References

• Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics–2013 update: a report from the American Heart Association. Circulation. 2013;127:e6–e245. doi:10.1161/CIR.0b013e31828124ad.
   PubMed Web of Science ®Google Scholar
• Mozaffarian D, Benjamin E, Go A, et al. Heart disease and stroke statistics–2015 update: a report from the American Heart Association. Circulation. 2015;131:e29–e322.
   PubMed Web of Science ®Google Scholar
• NINDS stroke information page (Stroke information page: National Institute of Neurological Disorders and Stroke (NINDS)); Available from: http://www.ninds.nih.gov/disorders/stroke/stroke.htm
   Google Scholar
• Soekadar S, Birbaumer N, Slutzky MW, et al. Brain-machine interfaces in neurorehabilitation of stroke. Neurobiol Dis. 2014 [cited 2014 Dec 7]. doi:10.1016/j.nbd.2014.11.025. ISSN 0969-9961.
   PubMed Web of Science ®Google Scholar
• Kelly-Hayes M, Beiser A, Kase C, et al. The influence of gender and age on disability following ischemic stroke: the Framingham study. J Stroke Cerebrovasc Dis. 2003;12:119–126.
   PubMedGoogle Scholar
• Soekadar SR, Birbaumer N, Cohen LG. Brain-computer-interfaces in the rehabilitation of stroke and neurotrauma. Psychology. 2011;48(6):1–19.
   Google Scholar
• Young B, Nigogosyan Z, Remsik A, et al. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Front Neuroeng. 2014;7:25.
   PubMedGoogle Scholar
• Ang K, Chua K, Phua K, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci. 2014;7(30). doi:10.1177/1550059414522229.
   PubMed Web of Science ®Google Scholar
• Reiss AP, Wolf SL, Hammel EA, et al. Constraint-induced movement therapy (CIMT): current perspectives and future directions. Stroke Res Treat. 2012;2012:159391. doi:10.1155/2012/159391.
   PubMedGoogle Scholar
• 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;27:53–62.
   PubMed Web of Science ®Google Scholar
• Bundy D, Wronkiewicz M, Sharma M, et al. Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain-computer interfaces in hemiplegic stroke survivors. J Neural Eng. 2012;9:12.
   Web of Science ®Google Scholar
• Young B, Nigogosyan Z, Walton L, et al. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng. 2014;7:26.
   PubMedGoogle Scholar
• Kaiser V, Kreilinger A, Müller-Putz GR, et al. First steps toward a motor imagery based stroke BCI: new strategy to set up a classifier. Front Neurosci. 2011;5:10.
   PubMed Web of Science ®Google Scholar
• Kasuga S, Matsushika Y, Kasashima-Shindo Y, et al. Transcranial direct current stimulation enhances mu rhythm desynchronization during motor imagery that depends on handedness. Laterality. 2015;9:1–16.
   Google Scholar
• Kreilinger A, Kaiser V, Breitwieser C, et al. Switching between manual control and brain-computer interface using long term and short term quality measures. Front Neurosci. 2012;6:11.
   PubMedGoogle Scholar
• Caria A, Weber C, Brötz D, et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology. 2011;48:578–582.
   PubMed Web of Science ®Google Scholar
• Shindo K, Kawashima K, Ushiba J, et al. Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. J Rehabil Med. 2011;43:951–957. doi:10.2340/16501977-0859.
   PubMed Web of Science ®Google Scholar
• Liu M, Fujiwara T, Shindo K, et al. Newer challenges to restore hemiparetic upper extremity after stroke: HANDS therapy and BMI neurorehabilitation. Hong Kong Physiother J. 2012;30:83–92. doi:10.1016/j.hkpj.2012.05.001.
   Google Scholar
• Muralidharan A, Chae J, Taylor DM. Extracting attempted hand movements from EEGs in people with complete hand paralysis following stroke. Front Neurosci. 2011;5:7.
   Web of Science ®Google Scholar
• Schalk G, Mellinger J. Practical guide to brain-computer interfacing with BCI2000: general-purpose software for brain-computer interface research, data acquisition, stimulus presentation, and brain monitoring. London: Springer; 2010. p. 1–260.
   Google Scholar
• Orihuela-Espina F, Del Castillo I, Palafox L, et al. Neural reorganization accompanying upper limb motor rehabilitation from stroke with virtual reality-based gesture therapy. Top Stroke Rehabil. 2013;20:197–209.
   PubMed Web of Science ®Google Scholar
• Prasad G, Herman P, Coyle D, et al. Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J Neuroeng Rehabil. 2010;7:17.
   PubMed Web of Science ®Google Scholar
• Carter AR, Connor LT, Dromerick AW. Rehabilitation after stroke: current state of the science. Curr Neurol Neurosci Rep. 2010;10:158–166.
   PubMed Web of Science ®Google Scholar
• Do AH, Wang PT, King CE, et al. Brain-computer interface controlled functional electrical stimulation system for ankle movement. J Neuroeng Rehabil. 2011;8:14.
   PubMed Web of Science ®Google Scholar
• Shelton FN, Reding MJ. Effect of lesion location on upper limb motor recovery after stroke. Stroke. 2001;32:107–112.
   PubMed Web of Science ®Google Scholar
• Cho W, Vidaurre C, Hoffmann U, et al. Afferent and efferent activity control in the design of brain computer interfaces for motor rehabilitation. In: Birbaumer N, Ramos-Murguialday A, editors. 33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS). Boston, MA: IEEE; 2011. p. 7310–7315.
   Google Scholar
• Wu C-Y, Huang P-C, Chen Y-T, et al. Effects of mirror therapy on motor and sensory recovery in chronic stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2013;94:1023–1030.
   PubMed Web of Science ®Google Scholar
• Nair V, Song J, Young B, et al. (2013) Changes in resting-state functional connectivity following BCI-EEG based intervention in sub-acute and chronic stroke patients. Radiological society of North America 2013 scientific assembly and annual meeting; Chicago (IL); 2013 Dec 1–6. http://archive.rsna.org/2013/13044452.html
   Google Scholar
• Wolf SL, Winstein CJ, Miller JP, et al. The EXCITE trial: retention of improved upper extremity function among stroke survivors receiving CI movement therapy. Lancet Neurol. 2008;7(1):33–40. doi:10.1016/S1474-4422(07)70294-6.
   PubMed Web of Science ®Google Scholar
• Gomez-Rodriguez M, Grosse-Wentrup M, Hill J, et al. Towards brain-robot interfaces in stroke rehabilitation. In: Gharabaghi A, Scholkopf B, Peters J, editors. IEEE International Conference on Rehabilitation Robotics (ICORR)/ International Neurorehabilitation Symposium (INRS)/International Conference on Virtual Rehabilitation (ICVR). Zurich: IEEE; 2011.
   Google Scholar
• Young B, Nigogosyan Z, Walton L, et al. Dose-response relationships using brain-computer interface technology impact stroke rehabilitation. Front Hum Neurosci. 2015;9:361.
   PubMed Web of Science ®Google Scholar
• Felton EA, Williams JC, Vanderheiden GC, et al. Mental workload during brain-computer interface training. Ergonomics. 2012;55:526–537.
   PubMed Web of Science ®Google Scholar
• Stoeckel L, Garrison K, Ghosh S, et al. Optimizing real time fMRI neurofeedback for therapeutic discovery and development. Neuroimage Clin. 2014;5:245–255.
   PubMed Web of Science ®Google Scholar
• Song J, Young B, Nigogosyan Z, et al. Characterizing relationships of DTI, fMRI, and motor recovery in stroke rehabilitation utilizing brain-computer interface technology. Front Neuroeng. 2014;7:31.
   PubMedGoogle Scholar
• Miralles F, Vargiu E, Dauwalder S, et al. Brain computer interface on track to home. Sci World J. 2015;2015:1–17. doi:10.1155/2015/623896.
   Google Scholar
• Sellers EW, Vaughan TM, Wolpaw JR. A brain-computer interface for long-term independent home use. Amyotroph Lateral Scler. 2010;11(5):449–455.
   PubMed Web of Science ®Google Scholar
• Young B, Nigogosyan Z, Nair V, et al. Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability. Front Neuroeng. 2014;7:18.
   PubMedGoogle Scholar
• Daly J, Cheng R, Rogers J, et al. 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;33:203–211.
   PubMed Web of Science ®Google Scholar
• Takahashi M, Takeda K, Otaka Y, et al. Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: a feasibility study. J Neuroeng Rehabil. 2012;9:6.
   PubMed Web of Science ®Google Scholar
• Biasiucci A, Leeb R, Al-Khodairy A, et al. Motor recovery after stroke by means of BCI-guided functional electrical stimulationat. Presented at: 5th International Brain-Computer Interface Meeting; 2013 Jun 3–7, 2013; Asilomar, CA. doi:10.3217/978-3-85125-260-6-49.
   Google Scholar
• Dromerick AW, Lang CE, Birkenmeier RL, et al. Very early constraint-induced movement during stroke rehabilitation (VECTORS) A single-center RCT. Neurology. 2009;73:195–201.
   PubMed Web of Science ®Google Scholar
• Lang KC, Thompson PA, Wolf SL. The EXCITE trial: reacquiring upper-extremity task performance with early versus late delivery of constraint therapy. Neurorehabil Neural Repair. 2013;27:654–663.
   PubMed Web of Science ®Google Scholar
• Ang K, Guan C, Chua K, et al. A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation. In: Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society. Minneapolis (MN): IEEE; 2009. p. 5981–5984.
   Google Scholar
• Ang K, Guan C, Chua K, et al. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. In: 32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10). Buenos Aires: IEEE; 2010. p. 5549–5552.48.
   Google Scholar
• Lo A, Guarino P, Richards L, et al. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362:1772–1783.
   PubMed Web of Science ®Google Scholar
• Wilson JA, Walton LM, Tyler M, et al. Lingual electrotactile stimulation as an alternative sensory feedback pathway for brain-computer interface applications. J Neural Eng. 2012;9:045007.
   PubMed Web of Science ®Google Scholar
• Buch E, Weber C, Cohen L, et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. 2008;39:910–917.
   PubMed Web of Science ®Google Scholar
• Broetz D, Braun C, Weber C, et al. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil Neural Repair. 2010;24:674–679.
   PubMed Web of Science ®Google Scholar
• Teo W-P, Chew E. Is motor-imagery brain-computer interface feasible in stroke rehabilitation? A narrative review. Pm R. 2014;6(8):723–728.
   PubMedGoogle Scholar
• 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;7:30.
   PubMedGoogle Scholar
• Mihara M, Hattori N, Hatakenaka M, et al. Near-infrared spectroscopy–mediated neurofeedback enhances efficacy of motor imagery–based training in poststroke victims: a pilot study. Stroke. 2013;44(4):1091–1098.
   PubMed Web of Science ®Google Scholar
• Naseer N, Hong K-S. Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface. Neurosci Lett. 2013;553:84–89. doi:10.1016/j.neulet.2013.08.021.
   PubMed Web of Science ®Google Scholar
• Kaiser V, Daly I, Pichiorri F, et al. Relationship between electrical brain responses to motor imagery and motor impairment in stroke. Stroke. 2012;43:2735–2740.
   PubMed Web of Science ®Google Scholar
• Tan H, Kong K, Shee C, et al. Post-acute stroke patients use brain-computer interface to activate electrical stimulation. In: 32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10). Buenos Aires: IEEE; 2010. p. 4234–4237.
   Google Scholar
• Nicolas-Alonso LF, Gomez-Gil J. Brain computer interfaces, a review. Sensors. 2012;12(2):1211–1279.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Ono T, Shindo K, Kawashima K, et al. Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front Neuroeng. 2014;7(19):1–8.
   PubMedGoogle Scholar
• Ang KK, Guan C, Phua KS, et al. Facilitating effects of transcranial direct current stimulation on motor imagery brain-computer interface with robotic feedback for stroke rehabilitation. Arch Phys Med Rehabil. 2015;96(3, Supplement):S79–S87.
   PubMed Web of Science ®Google Scholar
• Ahn M, Jun SC. Performance variation in motor imagery brain–computer interface: a brief review. J Neurosci Methods. 2015;243(0):103–110.
   PubMed Web of Science ®Google Scholar
• Cramer SC. Brain repair after stroke. N Engl J Med. 2010;362:1827–1829. doi:10.1056/NEJMe1003399.
   PubMed Web of Science ®Google Scholar
• Gomez-Rodriguez M, Peters J, Hill J, et al., et al. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. J Neural Eng. 2011;8:12.
   Web of Science ®Google Scholar
• Ang K, Guan C, Chua K, et al. A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface. Clin EEG Neurosci. 2011;42:253–258.
   PubMed Web of Science ®Google Scholar
• Ang KK, Guan C. Brain-computer interface in stroke rehabilitation. J Comput Sci Eng. 2013;7(2):139–146.
   Google Scholar
• Winstein CJ, Wolf SL, Dromerick AW, et al. Effect of a task-oriented rehabilitation program on upper extremity recovery following motor stroke: the ICARE randomized clinical trial. JAMA. 2016;315(6):571–581. doi:10.1001/jama.2016.0276.
   PubMed Web of Science ®Google Scholar
• Rayegani S, Raeissadat S, Sedighipour L, 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.
   PubMed Web of Science ®Google Scholar
• Song J, Nair V, Young B, et al. DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology. Front Hum Neurosci. 2015;9:195.
   PubMedGoogle Scholar
• Kim S, Park J, Jung M, et al. Effects of task-oriented training as an added treatment to electromyogram-triggered neuromuscular stimulation on upper extremity function in chronic stroke patients. Occup Ther Int. 2016. doi:10.1002/oti.1421.
   Google Scholar
• Shin J, Kim M, Lee J, et al. Effects of virtual reality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded, randomized controlled trial. J Neuroeng Rehabil. 2016;13(1). doi:10.1186/s12984-016-0125-x.
   PubMed Web of Science ®Google Scholar
• Lakshminarayanan K, Wang F, Webster JG, et al. Feasibility and usability of a wearable orthotic for stroke survivors with hand impairment. Disabil Rehabil Assist Technol. 2016;1–9. doi:10.3109/17483107.2015.1111945.
   PubMedGoogle Scholar