Narrative Review
Is Motor-Imagery Brain-Computer Interface Feasible in Stroke Rehabilitation? Wei-Peng Teo, PhD, Effie Chew, MBBS, MRCP In the past 3 decades, interest has increased in brain-computer interface (BCI) technology as a tool for assisting, augmenting, and rehabilitating sensorimotor functions in clinical populations. Initially designed as an assistive device for partial or total body impairments, BCI systems have since been explored as a possible adjuvant therapy in the rehabilitation of patients who have had a stroke. In particular, BCI systems incorporating a robotic manipulanda to passively manipulate affected limbs have been studied. These systems can use a range of invasive (ie, intracranial implanted electrodes) or noninvasive neurophysiologic recording techniques (ie, electroencephalography [EEG], near-infrared spectroscopy, and magnetoencephalography) to establish communication links between the brain and the BCI system. Trials are most commonly performed on EEG-based BCI in comparison with the other techniques because of its high temporal resolution, relatively low setup costs, portability, and noninvasive nature. EEG-based BCI detects event-related desynchronization/synchronization in sensorimotor oscillatory rhythms associated with motor imagery (MI), which in turn drives the BCI. Previous evidence suggests that the process of MI preferentially activates sensorimotor regions similar to actual task performance and that repeated practice of MI can induce plasticity changes in the brain. It is therefore postulated that the combination of MI and BCI may augment rehabilitation gains in patients who have had a stroke by activating corticomotor networks via MI and providing sensory feedback from the affected limb using end-effector robots. In this review we examine the current literature surrounding the feasibility of EEG-based MI-BCI systems in stroke rehabilitation. We also discuss the limitations of using EEG-based MI-BCI in patients who have had a stroke and suggest possible solutions to overcome these limitations. PM R 2014;6:723-728
INTRODUCTION Stroke is the second-largest cause of death worldwide and remains one of the leading causes of acquired disability in adults [1]. Although almost 85% of patients survive the initial injury [2], approximately 65% of stroke survivors will experience residual disabilities that impair daily function and quality of life [3], even after receiving standard medical treatment and rehabilitation. Among the plethora of physical disabilities, impairments to neuromuscular performance such as fine/gross motor control, muscle strength, and power are hallmarks of stroke that have the greatest impact on functional capacity [4]. Furthermore, depending on the area and extent of damage in the brain, these neuromuscular impairments are likely to result from a combination of symptoms such as hemiplegia, muscle weakness, numbness, reduced sensory feedback, muscle flaccidity, and spasticity [4]. To date, physical rehabilitation remains the preferred form of treatment to restore physical function and may be administered as soon as a patient is in a medically stable condition (inpatient treatment) [5,6] and continue after the patient is discharged from inpatient care (home-based/community treatment) [7-9]. The nature of any stroke rehabilitation program is largely task-specific (ie, training focuses on activities of daily living), and its effectiveness is intensity-dependent, with greater training intensities (ie, training volume and duration) often associated with better functional outcome [10]. However, because of its multidisciplinary nature, stroke rehabilitation is often labor-intensive and PM&R 1934-1482/14/$36.00 Printed in U.S.A.
W.-P.T. School of Medical and Applied Sciences, Central Queensland University, Bruce Highway, Rockhampton, Queensland, 4702, Australia. Address correspondence to: W.-P.T.; e-mail: [email protected] Disclosure: nothing to disclose E.C. Division of Neurology and Yong Loo Lin School of Medicine, National University Health Systems, Singapore Disclosure: nothing to disclose Submitted for publication July 4, 2013; accepted January 9, 2014.
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costly, which may have a negative impact on compliance with rehabilitation by patients once they are discharged. In recent years, trials have been conducted with novel methods of therapy in an attempt to improve patient compliance with rehabilitation programs. Such methods have included the use of off-the-shelf virtual reality and video games to increase patient motivation while attempting to minimize costs [11-13]; other trials have tested the use of neuromodulatory techniques (ie, transcranial magnetic stimulation and transcranial direct current stimulation [tDCS]) to augment existing physical therapy paradigms [14-16]. Among these novel therapies, brain-computer interface (BCI) technology that incorporates a robotic manipulanda (ie, a mechanical brace to physically manipulate the strokeaffected limb) is seen as a promising adjuvant therapy in stroke rehabilitation [17-22]. The concept of BCI is to use neurophysiologic signals during real-time motor imagery (MI) and to transform these signals into computer commands. These commands in turn drive a robotic manipulanda with the patient’s affected limb secured to it. Depending on the nature of these signals, different recording techniques such as electroencephalography (EEG), electrocorticography, magnetoencephalography, functional magnetic resonance imaging, positron emission tomography, and functional near-infrared spectroscopy [21,23] may serve as inputs for BCI systems, allowing the brain to volitionally interact with its external environment (Figure 1) [24-26]. Successful activation of a BCI system is dependent on the patient’s ability to produce vivid MI. MI refers to a process by which a person produces a mental picture of an intended motor task in the absence of physical motor output [27-29]; this process elicits corticomotor activation patterns similar to actual task performance [30,31]. MI also has the ability to affect neuroplastic changes after a period of training in healthy subjects [32,33] and in patients who have had a stroke [34], which is likely to facilitate functional gains during stroke rehabilitation [35,36]. As such, it is hypothesized that the combination of both techniques (MI and BCI) may be used as a potential therapy in stroke rehabilitation. The reason for combining the paradigms can be seen as a 2-tiered approach toward stroke rehabilitation: (1) activating central motoneuronal networks through the process of MI [37], thereby targeting neuroplasticity; and (2) providing sensorimotor feedback from the affect limb by using passive manipulation with the aid of an end-effector manipulanda. Although several MI-detection methods have been tested, this review will focus on EEG-based BCI because its low cost, portability, and noninvasive nature make it a suitable candidate in a clinical rehabilitation setting. In this review we will provide an overview of how the combination of MI and BCI may be used in stroke rehabilitation. We will also examine some of the limitations of current MI-BCI systems and suggest potential ways of overcoming these limitations.
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Figure 1. An example of motor imagery (MI) brain-computer interface training incorporating the use of electroencephalography (EEG) technology to provide neurophysiologic feedback and a robotic manipulanda for passive manipulation of the stroke-affected arm. The setup consists of the MIT-Manus robot [26] providing unrestricted unilateral movements in the horizontal plane during a computerized game and an EEG acquisition system to identify event-related desynchronization/ synchronization (ERD/ERS) EEG signals during MI. The game shown on the monitor indicates a nonrandomized, clockwise “reach-and-return” paradigm, with the aim of moving a yellow dot in the middle of the clock face to a separate red dot located along the circumference of the clock using MI. Upon the detection of ERD/ERS in EEG signals, the robotic manipulanda will provide passive movement of the stroke-affected arm to the intended point on the clock-face and back to the start point. The game interface can be changed accordingly to provide a more challenging and interactive option for patients as they progress with training.
MI IN STROKE REHABILITATION It has been suggested that MI may be beneficial in stroke rehabilitation by targeting the central motor system without physical initiation of a motor task [27]. Unlike active and passive movement therapies, the ability to perform MI is not dependent on residual function of the paretic limb. More importantly, the primary motor cortex is directly engaged in MI [38,39], as demonstrated by functional imaging studies on healthy subjects [40,41] and even in clinical populations [22,42-44]. A number of experiments have investigated the ability of patients who have had a stroke to perform MI. These studies showed that most patients who have had a stroke are able to perform MI despite chronic or severe motor impairments [45-47], and only patients with lesions in the parietal and frontal cortices [45,48] have increased difficulty in performing MI. This finding suggests that the corticomotor representation of movement associated with MI is not fully dependent on actual task performance after a stroke is sustained. As such, MI often has been tested in combination with conventional rehabilitation [29,35-37,47] and, more recently, robot-aided therapy [20,49-51]. Most notably, Page et al [42] provided evidence for the efficacy of MI in combination with conventional
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physiotherapy in patients in the chronic phase after a stroke. In this study, 32 patients with moderate impairments (based on upper-limb Fugl-Meyer scores) were randomized into 2 groups, with one group receiving standard physiotherapy and the other group receiving 30 minutes of MI training after physiotherapy. The group receiving MI training was instructed to emphasize activities of daily living. The results from this study showed an improvement in Fugl-Meyer Assessment scores in the group that received MI training combined with physiotherapy. Similar improvements in functional outcome measures also were observed in studies involving MI of the lower limb. Dunsky et al [36] investigated the effects of a home-based MI program in 17 patients in the chronic phase after a stroke. All patients received 15 minutes of supervised MI training 3 days a week for 6 weeks. The primary outcome measures of their study were spatiotemporal parameters and kinematic variables (ie, gait speed, stride length, cadence, and lower-limb joint angles during gait). The findings indicated that all patients improved on average by 40% in gait speed, and this improvement was maintained up to 3 weeks after assessment. Despite the lack of larger experiments, the findings from these studies suggest that MI training has the potential to be beneficial for motor recovery in persons who have had a stroke and that it complements traditional forms of physical therapy. Although the underlying mechanisms of MIassociated functional changes are unclear, it is likely that functional changes in persons who have had a stroke after MI training may be driven by the modulation of neuroplastic processes [34].
MI-BCI IN STROKE REHABILITATION In recent years, advancements in the spatiotemporal detection of event-related desynchronization/synchronization (ERD/ERS) [41,44,52] of EEG waves (ie, beta and mu oscillatory rhythms) have provided a more direct and accurate measure of neurophysiologic change during real and imagined movements [53,54]. By analyzing the change in ERD/ERS patterns in EEG signals during MI, MI-BCI systems are able to translate the imagination of movements into motor commands, which permits a patient to interact with his or her external environment [20,55]. It is postulated that the process of integrating motor intent with sensorimotor feedback via robot-assisted movement execution will augment neuroplasticity and remapping of corticomotor representation that is beneficial for stroke recovery [50]. One of the earliest attempts with MI-BCI in a stroke population, which involved patients with severe hand impairments, was reported by Birbaumer [23]. The aim of the study was to determine whether patients were able to produce a reliable MI-related ERD/ERS EEG patterns, which operated a hand orthotic robot. The results revealed that each subject required at least 10-20 sessions before he or she
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was able to successfully activate the BCI system. Although patients ultimately activated the BCI system, the fact that a significant number of sessions were required just to activate the system indicated that an extended period of training or familiarization was needed. Surprisingly, all patients reported significantly less hand spasticity; however, this finding was never formally quantified. Building on the findings of Birbaumer, several groups have further experimented with MI-BCI systems in stroke rehabilitation with varying results. Buch et al [18] reported that 6 of 8 patients with chronic hand plegia resulting from stroke could control the MI-BCI system after 13-22 sessions; however, none of the patients showed significant improvement in hand function after the BCI training. In contrast, Broetz et al [17,19] reported significant improvements in hand and arm movement ability, as well as speed and safety of gait, after 12 months of combined MI-BCI training and physical therapy in a patient in the chronic phase after a stroke. These results suggest that with a period of practice, most patients who have had a stroke are able to activate BCI systems using MI. The findings Broetz et al [17,19] were supported in a pilot trial by Ang et al [49] in a group of 8 patients who had a hemiparetic stroke and received 12 sessions (1-hour sessions, 3 times a week for 4 weeks) of robot-assisted upper-limb rehabilitation guided by an EEGbased MI-BCI system. The study by Ang et al showed that MI-BCI training resulted in a significant improvement in hand function assessed by the Fugl-Meyer Assessment. These preliminary reports demonstrate that MI-BCI has the potential to improve functional outcome in patients in the chronic phase of stroke, but its success may be highly dependent on the severity of motor impairments and the ability of patients to produce ERD/ERS of sensorimotor EEG rhythms during MI. Furthermore, the small sample size from these studies raises questions about the reliability of MI-BCI with patients who have had a stroke, and the long training duration required to activate the BCI system might be seen as unfavorable and less efficacious compared with conventional rehabilitation methods. In an effort to determine the reliability of MI-BCI in a stroke population, Ang et al [50] performed a larger clinical trial in which they assessed the ability of patients who had sustained a stroke to operate an EEG-based MI-BCI system. A total of 54 patients in the chronic phase after having a stroke who had mild to moderate impairments and 16 healthy age-matched control subjects were recruited to perform MI of a finger-tapping task to determine the extent of detectable EEG brain signals. The results showed that the accuracy of MI of the finger-tapping task in patients who had a stroke was comparable with that of healthy control subjects, and 89% of all patients successfully activated the MI-BCI system without much difficulty within 1 session. Furthermore, subsequent retest sessions confirmed that all patients who were successful in the first attempt were able to reproduce similar ERD/ERS EEG patterns. These findings
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provide strong support for the reliability and potential rehabilitative application of MI-BCI in a stroke population, particularly in patients with mild to moderate motor impairments.
LIMITATIONS OF MI-BCI Despite some preliminary success in clinical trials with MI-BCI in stroke rehabilitation, it is difficult to ascertain the efficacy of MI-BCI systems in a clinical setting because of the lack of long-term evidence to support its clinical relevance. It has been suggested that MI of the lower limbs (eg, foot sequences) may improve gait [35,36,56] and coordination of lower limb movements [57]; however, data for lower-limb MI-BCI studies have yet to be fully established. Recent analysis of EEG ERD/ERS during gait suggests that it is possible to find neural correlates of gait and to decode leg movement [58], but it remains unclear if engaging MIBCI training in gait rehabilitation will yield significant improvements. A major limitation with MI-BCI is the dependence on the subject’s ability to produce vivid MI and reliable ERD/ERS of EEG patterns. A study by Platz et al [59] suggests that mu ERD response may be altered after stroke and therefore may limit the potential for patients who have had a stroke to engage in MI-BCI training. In light of this problem, preliminary findings from 4 research groups showed that neuromodulatory techniques such as tDCS could potentiate ERD responses [60-63], thus leading to better MI accuracy. The findings of these 4 groups are highlighted in the next paragraph. Matsumoto et al [60] examined the modulation of ERD with anodal, cathodal, and sham tDCS of the M1 in 6 healthy subjects during MI of a hand movement task within a single-session design. Mu ERD significantly increased after anodal stimulation, whereas it significantly decreased after cathodal stimulation. Their findings were further supported by Tohyama et al [61], who measured mu ERD responses in 1 patient with severe motor impairments in the left arm after having a stroke. After 5 consecutive days of anodal tDCS to the ipsilesional M1, an increase in mu ERD was observed, similar to the findings reported by Matsumoto et al [60]. A later study by Kasashima et al [62] confirmed the results by Tohyama et al [61] when these investigators also demonstrated an increase in ERD of mu rhythm after anodal tDCS to the M1 in 6 moderately impaired patients who had a stroke. Recently, as part of an ongoing clinical trial, Ang et al [63] reported greater than average offline MI-associated EEG accuracy in patients who had a stroke and who received 20 minutes of real tDCS before MI-BCI training compared with patients receiving sham tDCS; however, it should be noted that the infancy of MI-BCI research and small sample size reported in these studies are limiting factors when one interprets the results. Further suggestions indicate that the modulation of sensorimotor rhythms may improve clinical
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motor outcome and the ability to produce ERD in sensorimotor rhythms correlated with better functional motor outcomes after a rehabilitation protocol in patients who have had a stroke [20,64]. Therefore it seems likely that neuromodulatory techniques such as tDCS may act as a primer for patients who have had a stroke before they receive MI-BCI training to enhance offline MI accuracy and potentiate ERD/ ERS EEG signals so as to better engage MI-BCI systems.
CONCLUSIONS AND FUTURE DIRECTIONS To date, the evidence from the literature suggests some promise for the feasibility and efficacy of MI-BCI in a rehabilitation setting. Although the preliminary findings of MIBCI in stroke rehabilitation have yielded mixed findings, the long-term effectiveness and efficacy of such systems remain in question. Longitudinal clinical data needs to be obtained on the effectiveness of MI-BCI to substantiate the current evidence. More importantly, the need exists to improve temporal and spatial resolution of different movement patterns performed in the same limb during EEG-based MI. Despite the lack of evidence pertaining to the combined use of NBS and MI-BCI, preliminary findings suggest that the pairing of both of these techniques may improve the mu ERD response that is necessary for BCI. It therefore remains to be seen whether future studies exploring the combined use of neuromodulatory techniques such as tDCS and MIBCI will result in better functional outcome in patients who have had a stroke.
ACKNOWLEDGMENTS We thank Drs Cuntai Guan and Kai-Keng Ang and Mr KokSoon Phua from the Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, for the figure and their contribution on the subject matter.
REFERENCES 1. World Health Organization. The 10 leading causes of death in the world, 2000 and 2011. Fact sheet, July 2013. Available at http://who. int/mediacentre/factsheets/fs310/en/. Accessed January 22, 2014. 2. Feigin VL, Forouzanfar MH, Krishnamurthi R, et al. Global and regional burden of stroke during 1990e2010: Findings from the Global Burden of Disease Study 2010. Lancet 2014;383:245-254. 3. Coffey CE, Cummings JL, Starkstein S, Robinson R. Stroke. In: Coffey CE, Cummings JL, eds. The American Psychiatric Press Textbook of Geriatric Neuropsychiatry. Washington, DC: American Psychiatric Publishing; 2000. 4. Odier C, Michel P. Common stroke syndromes. In: Brainin M, WolfDieter H, eds. Textbook of Stroke Medicine. New York: Cambridge University Press; 2009, 121-134. 5. Bates B, Choi JY, Duncan PW, et al. Veterans Affairs/Department of Defense Clinical Practice Guideline for the Management of Adult Stroke Rehabilitation Care: Executive summary. Stroke 2005;9:2049-2056. 6. Duncan PW, Zorowitz R, Bates B, et al. Management of Adult Stroke Rehabilitation Care: A clinical practice guideline. Stroke 2005;9: e100-e143.
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7. Baskett JJ, Broad JB, Reekie G, Hocking C, Green G. Shared responsibility for ongoing rehabilitation: A new approach to home-based therapy after stroke. Clin Rehabil 1999;1:23-33. 8. Gladman JR, Lincoln NB, Barer DH. A randomised controlled trial of domiciliary and hospital-based rehabilitation for stroke patients after discharge from hospital. J Neurol Neurosurg Psychiatry 1993; 9:960-966. 9. Young J, Forster A. Day hospital and home physiotherapy for stroke patients: A comparative cost-effectiveness study. J R Coll Physicians Lond 1993;3:252-258. 10. Car JH, Shepherd RB. Stroke rehabilitation: Guidelines for exercise and training to optimize motor skill. Edinburgh: Elsevier Science Limited; 2003. 11. Lee G. Effects of training using video games on the muscle strength, muscle tone, and activities of daily living of chronic stroke patients. J Phys Ther Sci 2013:595-597. 12. Sin H, lee G. Additional virtual reality training using Xbox Kinect in stroke survivors with hemiplegia. Am J Phys Med Rehabil 2013;92:871-880. 13. Lange B, Flynn S, Rizzo A. Initial usability assessment of off-the-shelf video game consoles for clinical game-based motor rehabilitation. Phys Ther Rev 2009;14:355-363. 14. Bastani A, Jaberzadeh S. Does anodal transcranial direct current stimulation enhance excitability of the motor cortex and motor function in healthy individuals and subjects with stroke: A systematic review and meta-analysis. Clin Neurophysiol 2012;4:644-657. 15. Kim DY, Lim JY, Kang EK, et al. Effect of transcranial direct current stimulation on motor recovery in patients with subacute stroke. Am J Phys Med Rehabil 2010;11:879-886. 16. Kim YH, You SH, Ko MH, et al. Repetitive transcranial magnetic stimulation-induced corticomotor excitability and associated motor skill acquisition in chronic stroke. Stroke 2006;6:1471-1476. 17. Broetz D, Braun C, Weber C, Soekadar SR, Caria A, Birbaumer N. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: A case report. Neurorehabil Neural Repair 2010;7:674-679. 18. Buch E, Weber C, Cohen LG, et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 2008; 3:910-917. 19. Caria A, Weber C, Brötz D, et al. Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology 2011;4:578-582. 20. 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;4:203-211. 21. Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 2008;11:1032-1043. 22. Kübler A, Nijboer F, Mellinger J, et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 2005;26:1775-1777. 23. Birbaumer N. Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 2006;6:517-532. 24. Allison BZ, Wolpaw EW, Wolpaw JR. Brain-computer interface systems: Progress and prospects. Expert Rev Med Devices 2007;4: 463-474. 25. Min BK, Marzelli MJ, Yoo SS. Neuroimaging-based approaches in the brain-computer interface. Trends Biotechnol 2010;11:552-560. 26. Aisen ML, Krebs HI, Hogan N, McDowell F, Volpe BT. The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. Arch Neurol 1997;4:443-446. 27. Jackson P. Potential role of mental practice using motor imagery in neurologic rehabilitation. Arch Phys Med Rehabil 2001;82: 1133-1141. 28. Decety J, Grezes J. Neural mechanisms subserving the perception of human actions. Trends Cogn Sci 1999;5:172-178. 29. Sharma N, Pomeroy VM, Baron JC. Motor imagery: A backdoor to the motor system after stroke? Stroke 2006;7:1941-1952.
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30. Gerardin E, Sirigu A, Lehericy S, et al. Partially overlapping neural networks for real and imagined hand movements. Cereb Cortex 2000; 11:1093-1104. 31. Hanakawa T, Immisch I, Toma K, Dimyan MA, Van Gelderen P, Hallett M. Functional properties of brain areas associated with motor execution and imagery. J Neurophysiol 2003;2:989-1002. 32. Facchini S, Muellbacher W, Battaglia F, Boroojerdi B, Hallett M. Focal enhancement of motor cortex excitability during motor imagery: A transcranial magnetic stimulation study. Acta Neurol Scand 2002;3: 146-151. 33. Bakker M, Overeem S, Snijders AH, et al. Motor imagery of foot dorsiflexion and gait: Effects on corticospinal excitability. Clin Neurophysiol 2008;11:2519-2527. 34. Cicinelli P, Marconi B, Zaccagnini M, Pasqualetti P, Filippi MM, Rossini PM. Imagery-induced cortical excitability changes in stroke: A transcranial magnetic stimulation study. Cereb Cortex 2006;2: 247-253. 35. Dunsky A, Dickstein R, Ariav C, Deutsch J, Marcovitz E. Motor imagery practice in gait rehabilitation of chronic post-stroke hemiparesis: Four case studies. Int J Rehabil Res 2006;4:351-356. 36. Dunsky A, Dickstein R, Marcovitz E, Levy S, Deutsch JE. Home-based motor imagery training for gait rehabilitation of people with chronic poststroke hemiparesis. Arch Phys Med Rehabil 2008;8:1580-1588. 37. Zimmermann-Schlatter A, Schuster C, Puhan MA, Siekierka E, Steurer J. Efficacy of motor imagery in post-stroke rehabilitation: A systematic review. J Neuroeng Rehabil 2008;5:8. 38. Carrillo-de-la-Peña MT, Galdo-Alvarez S, Lastra-Barreira C. Equivalent is not equal: Primary motor cortex (MI) activation during motor imagery and execution of sequential movements. Brain Res 2008;1226: 134-143. 39. Kranczioch C, Mathews S, Dean PJ, Sterr A. On the equivalence of executed and imagined movements: Evidence from lateralized motor and nonmotor potentials. Hum Brain Mapp 2009;10:3275-3286. 40. Pfurtscheller G, Neuper C. Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man. Neurosci Lett 1994;1:93-96. 41. Pfurtscheller G, Brunner C, Schlogl A, Lopes da Silva FH. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 2006;1:153-159. 42. Page SJ, Levine P, Leonard A. Mental practice in chronic stroke: Results of a randomized, placebo-controlled trial. Stroke 2007;4:1293-1297. 43. Scherer R, Mohapp A, Grieshofer P, Pfurtscheller G, Neuper C. Sensorimotor EEG patterns during motor imagery in hemiparetic stroke patients. Int J Bioelectromagn 2007;9:155-162. 44. Muller-Putz GR, Zimmermann D, Graimann B, Nestinger K, Korisek G, Pfurtscheller G. Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients. Brain Res 2007;1:84-91. 45. Johnson SH. Imagining the impossible: intact motor representations in hemiplegics. Neuroreport 2000;4:729-732. 46. Johnson SH, Sprehn G, Saykin AJ. Intact motor imagery in chronic upper limb hemiplegics: Evidence for activity-independent action representations. J Cogn Neurosci 2002;6:841-852. 47. Malouin F, Richards CL, Durand A, Doyon J. Clinical assessment of motor imagery after stroke. Neurorehabil Neural Repair 2008;4:330-340. 48. Sirigu A, Duhamel JR, Cohen L, Pillon B, Dubois B, Agid Y. The mental representation of hand movements after parietal cortex damage. Science 1996;273:1564-1568. 49. Ang KK, Guan C, Chua KS, et al. A clinical study of motor imagerybased brain-computer interface for upper limb robotic rehabilitation. Conf Proc IEEE Eng Med Biol Soc 2009;2009:5981-5984. 50. Ang KK, Guan C, Chua KS, 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;4:253-258. 51. Daly JJ, Fang Y, Perepezko EM, Siemionow V, Yue GH. Prolonged cognitive planning time, elevated cognitive effort, and relationship to
728
52.
53.
54.
55.
56. 57. 58.
Teo and Chew
coordination and motor control following stroke. IEEE Trans Neural Syst Rehabil Eng 2006;2:168-171. Pfurtscheller G, Neuper C. Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments. Prog Brain Res 2006; 159:433-437. McFarland DJ, Miner LA, Vaughan TM, Wolpaw JR. Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr 2000;3:177-186. Stavrinou ML, Moraru L, Cimponeriu L, Della Penna S, Bezerianos A. Evaluation of cortical connectivity during real and imagined rhythmic finger tapping. Brain Topogr 2006;19:137-145. Friehs GM, Zerris VA, Ojakangas CL, Fellows MR, Donoghue JP. Brainmachine and brain-computer interfaces. Stroke 2004;11(suppl 1): 2702-2705. Dickstein R, Deutsch JE. Motor imagery in physical therapist practice. Phys Ther 2007;7:942-953. Malouin F, Richards CL. Mental practice for relearning locomotor skills. Phys Ther 2010;2:240-251. Presacco A, Forrester L, Contreras-Vidal JL. Towards a non-invasive brain-machine interface system to restore gait function in humans. Conf Proc IEEE Eng Med Biol Soc 2011;2011:4588-4591.
MOTOR-IMAGERY BCI IN STROKE REHABILITATION
59. 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. 60. Matsumoto J, Fujiwara T, Takahashi O, Liu M, Kimura A, Ushiba J. Modulation of mu rhythm desynchronization during motor imagery by transcranial direct current stimulation. J Neuroeng Rehabil 2010;7:27. 61. Tohyama T, Fujiwara T, Matsumoto J, et al. Modulation of eventrelated desynchronization during motor imagery with transcranial direct current stimulation in a patient with severe hemiparetic stroke: A case report. Keio J Med 2011;4:114-118. 62. Kasashima Y, Fujiwara T, Matsushika Y, et al. Modulation of eventrelated desynchronization during motor imagery with transcranial direct current stimulation (tDCS) in patients with chronic hemiparetic stroke. Exp Brain Res 2012;3:263-268. 63. Ang KK, Guan C, Phua KS, et al. Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation. Conf Proc IEEE Eng Med Biol Soc 2012;2012:4128-4131. 64. Platz T, Kim IH, Engel U, Kieselbach A, Mauritz KH. Brain activation pattern as assessed with multi-modal EEG analysis predict motor recovery among stroke patients with mild arm paresis who receive the Arm Ability Training. Restor Neurol Neurosci 2002;20:21-35.
Is Motor-Imagery Brain-Computer Interface Feasible in Stroke Rehabilitation? Wei-Peng Teo, PhD, Effie Chew, MBBS, MRCP In the past 3 decades, interest has increased in brain-computer interface (BCI) technology as a tool for assisting, augmenting, and rehabilitating sensorimotor functions in clinical populations. Initially designed as an assistive device for partial or total body impairments, BCI systems have since been explored as a possible adjuvant therapy in the rehabilitation of patients who have had a stroke. In particular, BCI systems incorporating a robotic manipulanda to passively manipulate affected limbs have been studied. These systems can use a range of invasive (ie, intracranial implanted electrodes) or noninvasive neurophysiologic recording techniques (ie, electroencephalography [EEG], near-infrared spectroscopy, and magnetoencephalography) to establish communication links between the brain and the BCI system. Trials are most commonly performed on EEG-based BCI in comparison with the other techniques because of its high temporal resolution, relatively low setup costs, portability, and noninvasive nature. EEG-based BCI detects event-related desynchronization/synchronization in sensorimotor oscillatory rhythms associated with motor imagery (MI), which in turn drives the BCI. Previous evidence suggests that the process of MI preferentially activates sensorimotor regions similar to actual task performance and that repeated practice of MI can induce plasticity changes in the brain. It is therefore postulated that the combination of MI and BCI may augment rehabilitation gains in patients who have had a stroke by activating corticomotor networks via MI and providing sensory feedback from the affected limb using end-effector robots. In this review we examine the current literature surrounding the feasibility of EEG-based MI-BCI systems in stroke rehabilitation. We also discuss the limitations of using EEG-based MI-BCI in patients who have had a stroke and suggest possible solutions to overcome these limitations. PM R 2014;6:723-728
INTRODUCTION Stroke is the second-largest cause of death worldwide and remains one of the leading causes of acquired disability in adults [1]. Although almost 85% of patients survive the initial injury [2], approximately 65% of stroke survivors will experience residual disabilities that impair daily function and quality of life [3], even after receiving standard medical treatment and rehabilitation. Among the plethora of physical disabilities, impairments to neuromuscular performance such as fine/gross motor control, muscle strength, and power are hallmarks of stroke that have the greatest impact on functional capacity [4]. Furthermore, depending on the area and extent of damage in the brain, these neuromuscular impairments are likely to result from a combination of symptoms such as hemiplegia, muscle weakness, numbness, reduced sensory feedback, muscle flaccidity, and spasticity [4]. To date, physical rehabilitation remains the preferred form of treatment to restore physical function and may be administered as soon as a patient is in a medically stable condition (inpatient treatment) [5,6] and continue after the patient is discharged from inpatient care (home-based/community treatment) [7-9]. The nature of any stroke rehabilitation program is largely task-specific (ie, training focuses on activities of daily living), and its effectiveness is intensity-dependent, with greater training intensities (ie, training volume and duration) often associated with better functional outcome [10]. However, because of its multidisciplinary nature, stroke rehabilitation is often labor-intensive and PM&R 1934-1482/14/$36.00 Printed in U.S.A.
W.-P.T. School of Medical and Applied Sciences, Central Queensland University, Bruce Highway, Rockhampton, Queensland, 4702, Australia. Address correspondence to: W.-P.T.; e-mail: [email protected] Disclosure: nothing to disclose E.C. Division of Neurology and Yong Loo Lin School of Medicine, National University Health Systems, Singapore Disclosure: nothing to disclose Submitted for publication July 4, 2013; accepted January 9, 2014.
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costly, which may have a negative impact on compliance with rehabilitation by patients once they are discharged. In recent years, trials have been conducted with novel methods of therapy in an attempt to improve patient compliance with rehabilitation programs. Such methods have included the use of off-the-shelf virtual reality and video games to increase patient motivation while attempting to minimize costs [11-13]; other trials have tested the use of neuromodulatory techniques (ie, transcranial magnetic stimulation and transcranial direct current stimulation [tDCS]) to augment existing physical therapy paradigms [14-16]. Among these novel therapies, brain-computer interface (BCI) technology that incorporates a robotic manipulanda (ie, a mechanical brace to physically manipulate the strokeaffected limb) is seen as a promising adjuvant therapy in stroke rehabilitation [17-22]. The concept of BCI is to use neurophysiologic signals during real-time motor imagery (MI) and to transform these signals into computer commands. These commands in turn drive a robotic manipulanda with the patient’s affected limb secured to it. Depending on the nature of these signals, different recording techniques such as electroencephalography (EEG), electrocorticography, magnetoencephalography, functional magnetic resonance imaging, positron emission tomography, and functional near-infrared spectroscopy [21,23] may serve as inputs for BCI systems, allowing the brain to volitionally interact with its external environment (Figure 1) [24-26]. Successful activation of a BCI system is dependent on the patient’s ability to produce vivid MI. MI refers to a process by which a person produces a mental picture of an intended motor task in the absence of physical motor output [27-29]; this process elicits corticomotor activation patterns similar to actual task performance [30,31]. MI also has the ability to affect neuroplastic changes after a period of training in healthy subjects [32,33] and in patients who have had a stroke [34], which is likely to facilitate functional gains during stroke rehabilitation [35,36]. As such, it is hypothesized that the combination of both techniques (MI and BCI) may be used as a potential therapy in stroke rehabilitation. The reason for combining the paradigms can be seen as a 2-tiered approach toward stroke rehabilitation: (1) activating central motoneuronal networks through the process of MI [37], thereby targeting neuroplasticity; and (2) providing sensorimotor feedback from the affect limb by using passive manipulation with the aid of an end-effector manipulanda. Although several MI-detection methods have been tested, this review will focus on EEG-based BCI because its low cost, portability, and noninvasive nature make it a suitable candidate in a clinical rehabilitation setting. In this review we will provide an overview of how the combination of MI and BCI may be used in stroke rehabilitation. We will also examine some of the limitations of current MI-BCI systems and suggest potential ways of overcoming these limitations.
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Figure 1. An example of motor imagery (MI) brain-computer interface training incorporating the use of electroencephalography (EEG) technology to provide neurophysiologic feedback and a robotic manipulanda for passive manipulation of the stroke-affected arm. The setup consists of the MIT-Manus robot [26] providing unrestricted unilateral movements in the horizontal plane during a computerized game and an EEG acquisition system to identify event-related desynchronization/ synchronization (ERD/ERS) EEG signals during MI. The game shown on the monitor indicates a nonrandomized, clockwise “reach-and-return” paradigm, with the aim of moving a yellow dot in the middle of the clock face to a separate red dot located along the circumference of the clock using MI. Upon the detection of ERD/ERS in EEG signals, the robotic manipulanda will provide passive movement of the stroke-affected arm to the intended point on the clock-face and back to the start point. The game interface can be changed accordingly to provide a more challenging and interactive option for patients as they progress with training.
MI IN STROKE REHABILITATION It has been suggested that MI may be beneficial in stroke rehabilitation by targeting the central motor system without physical initiation of a motor task [27]. Unlike active and passive movement therapies, the ability to perform MI is not dependent on residual function of the paretic limb. More importantly, the primary motor cortex is directly engaged in MI [38,39], as demonstrated by functional imaging studies on healthy subjects [40,41] and even in clinical populations [22,42-44]. A number of experiments have investigated the ability of patients who have had a stroke to perform MI. These studies showed that most patients who have had a stroke are able to perform MI despite chronic or severe motor impairments [45-47], and only patients with lesions in the parietal and frontal cortices [45,48] have increased difficulty in performing MI. This finding suggests that the corticomotor representation of movement associated with MI is not fully dependent on actual task performance after a stroke is sustained. As such, MI often has been tested in combination with conventional rehabilitation [29,35-37,47] and, more recently, robot-aided therapy [20,49-51]. Most notably, Page et al [42] provided evidence for the efficacy of MI in combination with conventional
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physiotherapy in patients in the chronic phase after a stroke. In this study, 32 patients with moderate impairments (based on upper-limb Fugl-Meyer scores) were randomized into 2 groups, with one group receiving standard physiotherapy and the other group receiving 30 minutes of MI training after physiotherapy. The group receiving MI training was instructed to emphasize activities of daily living. The results from this study showed an improvement in Fugl-Meyer Assessment scores in the group that received MI training combined with physiotherapy. Similar improvements in functional outcome measures also were observed in studies involving MI of the lower limb. Dunsky et al [36] investigated the effects of a home-based MI program in 17 patients in the chronic phase after a stroke. All patients received 15 minutes of supervised MI training 3 days a week for 6 weeks. The primary outcome measures of their study were spatiotemporal parameters and kinematic variables (ie, gait speed, stride length, cadence, and lower-limb joint angles during gait). The findings indicated that all patients improved on average by 40% in gait speed, and this improvement was maintained up to 3 weeks after assessment. Despite the lack of larger experiments, the findings from these studies suggest that MI training has the potential to be beneficial for motor recovery in persons who have had a stroke and that it complements traditional forms of physical therapy. Although the underlying mechanisms of MIassociated functional changes are unclear, it is likely that functional changes in persons who have had a stroke after MI training may be driven by the modulation of neuroplastic processes [34].
MI-BCI IN STROKE REHABILITATION In recent years, advancements in the spatiotemporal detection of event-related desynchronization/synchronization (ERD/ERS) [41,44,52] of EEG waves (ie, beta and mu oscillatory rhythms) have provided a more direct and accurate measure of neurophysiologic change during real and imagined movements [53,54]. By analyzing the change in ERD/ERS patterns in EEG signals during MI, MI-BCI systems are able to translate the imagination of movements into motor commands, which permits a patient to interact with his or her external environment [20,55]. It is postulated that the process of integrating motor intent with sensorimotor feedback via robot-assisted movement execution will augment neuroplasticity and remapping of corticomotor representation that is beneficial for stroke recovery [50]. One of the earliest attempts with MI-BCI in a stroke population, which involved patients with severe hand impairments, was reported by Birbaumer [23]. The aim of the study was to determine whether patients were able to produce a reliable MI-related ERD/ERS EEG patterns, which operated a hand orthotic robot. The results revealed that each subject required at least 10-20 sessions before he or she
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was able to successfully activate the BCI system. Although patients ultimately activated the BCI system, the fact that a significant number of sessions were required just to activate the system indicated that an extended period of training or familiarization was needed. Surprisingly, all patients reported significantly less hand spasticity; however, this finding was never formally quantified. Building on the findings of Birbaumer, several groups have further experimented with MI-BCI systems in stroke rehabilitation with varying results. Buch et al [18] reported that 6 of 8 patients with chronic hand plegia resulting from stroke could control the MI-BCI system after 13-22 sessions; however, none of the patients showed significant improvement in hand function after the BCI training. In contrast, Broetz et al [17,19] reported significant improvements in hand and arm movement ability, as well as speed and safety of gait, after 12 months of combined MI-BCI training and physical therapy in a patient in the chronic phase after a stroke. These results suggest that with a period of practice, most patients who have had a stroke are able to activate BCI systems using MI. The findings Broetz et al [17,19] were supported in a pilot trial by Ang et al [49] in a group of 8 patients who had a hemiparetic stroke and received 12 sessions (1-hour sessions, 3 times a week for 4 weeks) of robot-assisted upper-limb rehabilitation guided by an EEGbased MI-BCI system. The study by Ang et al showed that MI-BCI training resulted in a significant improvement in hand function assessed by the Fugl-Meyer Assessment. These preliminary reports demonstrate that MI-BCI has the potential to improve functional outcome in patients in the chronic phase of stroke, but its success may be highly dependent on the severity of motor impairments and the ability of patients to produce ERD/ERS of sensorimotor EEG rhythms during MI. Furthermore, the small sample size from these studies raises questions about the reliability of MI-BCI with patients who have had a stroke, and the long training duration required to activate the BCI system might be seen as unfavorable and less efficacious compared with conventional rehabilitation methods. In an effort to determine the reliability of MI-BCI in a stroke population, Ang et al [50] performed a larger clinical trial in which they assessed the ability of patients who had sustained a stroke to operate an EEG-based MI-BCI system. A total of 54 patients in the chronic phase after having a stroke who had mild to moderate impairments and 16 healthy age-matched control subjects were recruited to perform MI of a finger-tapping task to determine the extent of detectable EEG brain signals. The results showed that the accuracy of MI of the finger-tapping task in patients who had a stroke was comparable with that of healthy control subjects, and 89% of all patients successfully activated the MI-BCI system without much difficulty within 1 session. Furthermore, subsequent retest sessions confirmed that all patients who were successful in the first attempt were able to reproduce similar ERD/ERS EEG patterns. These findings
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provide strong support for the reliability and potential rehabilitative application of MI-BCI in a stroke population, particularly in patients with mild to moderate motor impairments.
LIMITATIONS OF MI-BCI Despite some preliminary success in clinical trials with MI-BCI in stroke rehabilitation, it is difficult to ascertain the efficacy of MI-BCI systems in a clinical setting because of the lack of long-term evidence to support its clinical relevance. It has been suggested that MI of the lower limbs (eg, foot sequences) may improve gait [35,36,56] and coordination of lower limb movements [57]; however, data for lower-limb MI-BCI studies have yet to be fully established. Recent analysis of EEG ERD/ERS during gait suggests that it is possible to find neural correlates of gait and to decode leg movement [58], but it remains unclear if engaging MIBCI training in gait rehabilitation will yield significant improvements. A major limitation with MI-BCI is the dependence on the subject’s ability to produce vivid MI and reliable ERD/ERS of EEG patterns. A study by Platz et al [59] suggests that mu ERD response may be altered after stroke and therefore may limit the potential for patients who have had a stroke to engage in MI-BCI training. In light of this problem, preliminary findings from 4 research groups showed that neuromodulatory techniques such as tDCS could potentiate ERD responses [60-63], thus leading to better MI accuracy. The findings of these 4 groups are highlighted in the next paragraph. Matsumoto et al [60] examined the modulation of ERD with anodal, cathodal, and sham tDCS of the M1 in 6 healthy subjects during MI of a hand movement task within a single-session design. Mu ERD significantly increased after anodal stimulation, whereas it significantly decreased after cathodal stimulation. Their findings were further supported by Tohyama et al [61], who measured mu ERD responses in 1 patient with severe motor impairments in the left arm after having a stroke. After 5 consecutive days of anodal tDCS to the ipsilesional M1, an increase in mu ERD was observed, similar to the findings reported by Matsumoto et al [60]. A later study by Kasashima et al [62] confirmed the results by Tohyama et al [61] when these investigators also demonstrated an increase in ERD of mu rhythm after anodal tDCS to the M1 in 6 moderately impaired patients who had a stroke. Recently, as part of an ongoing clinical trial, Ang et al [63] reported greater than average offline MI-associated EEG accuracy in patients who had a stroke and who received 20 minutes of real tDCS before MI-BCI training compared with patients receiving sham tDCS; however, it should be noted that the infancy of MI-BCI research and small sample size reported in these studies are limiting factors when one interprets the results. Further suggestions indicate that the modulation of sensorimotor rhythms may improve clinical
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motor outcome and the ability to produce ERD in sensorimotor rhythms correlated with better functional motor outcomes after a rehabilitation protocol in patients who have had a stroke [20,64]. Therefore it seems likely that neuromodulatory techniques such as tDCS may act as a primer for patients who have had a stroke before they receive MI-BCI training to enhance offline MI accuracy and potentiate ERD/ ERS EEG signals so as to better engage MI-BCI systems.
CONCLUSIONS AND FUTURE DIRECTIONS To date, the evidence from the literature suggests some promise for the feasibility and efficacy of MI-BCI in a rehabilitation setting. Although the preliminary findings of MIBCI in stroke rehabilitation have yielded mixed findings, the long-term effectiveness and efficacy of such systems remain in question. Longitudinal clinical data needs to be obtained on the effectiveness of MI-BCI to substantiate the current evidence. More importantly, the need exists to improve temporal and spatial resolution of different movement patterns performed in the same limb during EEG-based MI. Despite the lack of evidence pertaining to the combined use of NBS and MI-BCI, preliminary findings suggest that the pairing of both of these techniques may improve the mu ERD response that is necessary for BCI. It therefore remains to be seen whether future studies exploring the combined use of neuromodulatory techniques such as tDCS and MIBCI will result in better functional outcome in patients who have had a stroke.
ACKNOWLEDGMENTS We thank Drs Cuntai Guan and Kai-Keng Ang and Mr KokSoon Phua from the Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, for the figure and their contribution on the subject matter.
REFERENCES 1. World Health Organization. The 10 leading causes of death in the world, 2000 and 2011. Fact sheet, July 2013. Available at http://who. int/mediacentre/factsheets/fs310/en/. Accessed January 22, 2014. 2. Feigin VL, Forouzanfar MH, Krishnamurthi R, et al. Global and regional burden of stroke during 1990e2010: Findings from the Global Burden of Disease Study 2010. Lancet 2014;383:245-254. 3. Coffey CE, Cummings JL, Starkstein S, Robinson R. Stroke. In: Coffey CE, Cummings JL, eds. The American Psychiatric Press Textbook of Geriatric Neuropsychiatry. Washington, DC: American Psychiatric Publishing; 2000. 4. Odier C, Michel P. Common stroke syndromes. In: Brainin M, WolfDieter H, eds. Textbook of Stroke Medicine. New York: Cambridge University Press; 2009, 121-134. 5. Bates B, Choi JY, Duncan PW, et al. Veterans Affairs/Department of Defense Clinical Practice Guideline for the Management of Adult Stroke Rehabilitation Care: Executive summary. Stroke 2005;9:2049-2056. 6. Duncan PW, Zorowitz R, Bates B, et al. Management of Adult Stroke Rehabilitation Care: A clinical practice guideline. Stroke 2005;9: e100-e143.
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7. Baskett JJ, Broad JB, Reekie G, Hocking C, Green G. Shared responsibility for ongoing rehabilitation: A new approach to home-based therapy after stroke. Clin Rehabil 1999;1:23-33. 8. Gladman JR, Lincoln NB, Barer DH. A randomised controlled trial of domiciliary and hospital-based rehabilitation for stroke patients after discharge from hospital. J Neurol Neurosurg Psychiatry 1993; 9:960-966. 9. Young J, Forster A. Day hospital and home physiotherapy for stroke patients: A comparative cost-effectiveness study. J R Coll Physicians Lond 1993;3:252-258. 10. Car JH, Shepherd RB. Stroke rehabilitation: Guidelines for exercise and training to optimize motor skill. Edinburgh: Elsevier Science Limited; 2003. 11. Lee G. Effects of training using video games on the muscle strength, muscle tone, and activities of daily living of chronic stroke patients. J Phys Ther Sci 2013:595-597. 12. Sin H, lee G. Additional virtual reality training using Xbox Kinect in stroke survivors with hemiplegia. Am J Phys Med Rehabil 2013;92:871-880. 13. Lange B, Flynn S, Rizzo A. Initial usability assessment of off-the-shelf video game consoles for clinical game-based motor rehabilitation. Phys Ther Rev 2009;14:355-363. 14. Bastani A, Jaberzadeh S. Does anodal transcranial direct current stimulation enhance excitability of the motor cortex and motor function in healthy individuals and subjects with stroke: A systematic review and meta-analysis. Clin Neurophysiol 2012;4:644-657. 15. Kim DY, Lim JY, Kang EK, et al. Effect of transcranial direct current stimulation on motor recovery in patients with subacute stroke. Am J Phys Med Rehabil 2010;11:879-886. 16. Kim YH, You SH, Ko MH, et al. Repetitive transcranial magnetic stimulation-induced corticomotor excitability and associated motor skill acquisition in chronic stroke. Stroke 2006;6:1471-1476. 17. Broetz D, Braun C, Weber C, Soekadar SR, Caria A, Birbaumer N. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: A case report. Neurorehabil Neural Repair 2010;7:674-679. 18. Buch E, Weber C, Cohen LG, et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 2008; 3:910-917. 19. Caria A, Weber C, Brötz D, et al. Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology 2011;4:578-582. 20. 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;4:203-211. 21. Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 2008;11:1032-1043. 22. Kübler A, Nijboer F, Mellinger J, et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 2005;26:1775-1777. 23. Birbaumer N. Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 2006;6:517-532. 24. Allison BZ, Wolpaw EW, Wolpaw JR. Brain-computer interface systems: Progress and prospects. Expert Rev Med Devices 2007;4: 463-474. 25. Min BK, Marzelli MJ, Yoo SS. Neuroimaging-based approaches in the brain-computer interface. Trends Biotechnol 2010;11:552-560. 26. Aisen ML, Krebs HI, Hogan N, McDowell F, Volpe BT. The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. Arch Neurol 1997;4:443-446. 27. Jackson P. Potential role of mental practice using motor imagery in neurologic rehabilitation. Arch Phys Med Rehabil 2001;82: 1133-1141. 28. Decety J, Grezes J. Neural mechanisms subserving the perception of human actions. Trends Cogn Sci 1999;5:172-178. 29. Sharma N, Pomeroy VM, Baron JC. Motor imagery: A backdoor to the motor system after stroke? Stroke 2006;7:1941-1952.
Vol. 6, Iss. 8, 2014
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30. Gerardin E, Sirigu A, Lehericy S, et al. Partially overlapping neural networks for real and imagined hand movements. Cereb Cortex 2000; 11:1093-1104. 31. Hanakawa T, Immisch I, Toma K, Dimyan MA, Van Gelderen P, Hallett M. Functional properties of brain areas associated with motor execution and imagery. J Neurophysiol 2003;2:989-1002. 32. Facchini S, Muellbacher W, Battaglia F, Boroojerdi B, Hallett M. Focal enhancement of motor cortex excitability during motor imagery: A transcranial magnetic stimulation study. Acta Neurol Scand 2002;3: 146-151. 33. Bakker M, Overeem S, Snijders AH, et al. Motor imagery of foot dorsiflexion and gait: Effects on corticospinal excitability. Clin Neurophysiol 2008;11:2519-2527. 34. Cicinelli P, Marconi B, Zaccagnini M, Pasqualetti P, Filippi MM, Rossini PM. Imagery-induced cortical excitability changes in stroke: A transcranial magnetic stimulation study. Cereb Cortex 2006;2: 247-253. 35. Dunsky A, Dickstein R, Ariav C, Deutsch J, Marcovitz E. Motor imagery practice in gait rehabilitation of chronic post-stroke hemiparesis: Four case studies. Int J Rehabil Res 2006;4:351-356. 36. Dunsky A, Dickstein R, Marcovitz E, Levy S, Deutsch JE. Home-based motor imagery training for gait rehabilitation of people with chronic poststroke hemiparesis. Arch Phys Med Rehabil 2008;8:1580-1588. 37. Zimmermann-Schlatter A, Schuster C, Puhan MA, Siekierka E, Steurer J. Efficacy of motor imagery in post-stroke rehabilitation: A systematic review. J Neuroeng Rehabil 2008;5:8. 38. Carrillo-de-la-Peña MT, Galdo-Alvarez S, Lastra-Barreira C. Equivalent is not equal: Primary motor cortex (MI) activation during motor imagery and execution of sequential movements. Brain Res 2008;1226: 134-143. 39. Kranczioch C, Mathews S, Dean PJ, Sterr A. On the equivalence of executed and imagined movements: Evidence from lateralized motor and nonmotor potentials. Hum Brain Mapp 2009;10:3275-3286. 40. Pfurtscheller G, Neuper C. Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man. Neurosci Lett 1994;1:93-96. 41. Pfurtscheller G, Brunner C, Schlogl A, Lopes da Silva FH. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 2006;1:153-159. 42. Page SJ, Levine P, Leonard A. Mental practice in chronic stroke: Results of a randomized, placebo-controlled trial. Stroke 2007;4:1293-1297. 43. Scherer R, Mohapp A, Grieshofer P, Pfurtscheller G, Neuper C. Sensorimotor EEG patterns during motor imagery in hemiparetic stroke patients. Int J Bioelectromagn 2007;9:155-162. 44. Muller-Putz GR, Zimmermann D, Graimann B, Nestinger K, Korisek G, Pfurtscheller G. Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients. Brain Res 2007;1:84-91. 45. Johnson SH. Imagining the impossible: intact motor representations in hemiplegics. Neuroreport 2000;4:729-732. 46. Johnson SH, Sprehn G, Saykin AJ. Intact motor imagery in chronic upper limb hemiplegics: Evidence for activity-independent action representations. J Cogn Neurosci 2002;6:841-852. 47. Malouin F, Richards CL, Durand A, Doyon J. Clinical assessment of motor imagery after stroke. Neurorehabil Neural Repair 2008;4:330-340. 48. Sirigu A, Duhamel JR, Cohen L, Pillon B, Dubois B, Agid Y. The mental representation of hand movements after parietal cortex damage. Science 1996;273:1564-1568. 49. Ang KK, Guan C, Chua KS, et al. A clinical study of motor imagerybased brain-computer interface for upper limb robotic rehabilitation. Conf Proc IEEE Eng Med Biol Soc 2009;2009:5981-5984. 50. Ang KK, Guan C, Chua KS, 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;4:253-258. 51. Daly JJ, Fang Y, Perepezko EM, Siemionow V, Yue GH. Prolonged cognitive planning time, elevated cognitive effort, and relationship to
728
52.
53.
54.
55.
56. 57. 58.
Teo and Chew
coordination and motor control following stroke. IEEE Trans Neural Syst Rehabil Eng 2006;2:168-171. Pfurtscheller G, Neuper C. Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments. Prog Brain Res 2006; 159:433-437. McFarland DJ, Miner LA, Vaughan TM, Wolpaw JR. Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr 2000;3:177-186. Stavrinou ML, Moraru L, Cimponeriu L, Della Penna S, Bezerianos A. Evaluation of cortical connectivity during real and imagined rhythmic finger tapping. Brain Topogr 2006;19:137-145. Friehs GM, Zerris VA, Ojakangas CL, Fellows MR, Donoghue JP. Brainmachine and brain-computer interfaces. Stroke 2004;11(suppl 1): 2702-2705. Dickstein R, Deutsch JE. Motor imagery in physical therapist practice. Phys Ther 2007;7:942-953. Malouin F, Richards CL. Mental practice for relearning locomotor skills. Phys Ther 2010;2:240-251. Presacco A, Forrester L, Contreras-Vidal JL. Towards a non-invasive brain-machine interface system to restore gait function in humans. Conf Proc IEEE Eng Med Biol Soc 2011;2011:4588-4591.
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59. 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. 60. Matsumoto J, Fujiwara T, Takahashi O, Liu M, Kimura A, Ushiba J. Modulation of mu rhythm desynchronization during motor imagery by transcranial direct current stimulation. J Neuroeng Rehabil 2010;7:27. 61. Tohyama T, Fujiwara T, Matsumoto J, et al. Modulation of eventrelated desynchronization during motor imagery with transcranial direct current stimulation in a patient with severe hemiparetic stroke: A case report. Keio J Med 2011;4:114-118. 62. Kasashima Y, Fujiwara T, Matsushika Y, et al. Modulation of eventrelated desynchronization during motor imagery with transcranial direct current stimulation (tDCS) in patients with chronic hemiparetic stroke. Exp Brain Res 2012;3:263-268. 63. Ang KK, Guan C, Phua KS, et al. Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation. Conf Proc IEEE Eng Med Biol Soc 2012;2012:4128-4131. 64. Platz T, Kim IH, Engel U, Kieselbach A, Mauritz KH. Brain activation pattern as assessed with multi-modal EEG analysis predict motor recovery among stroke patients with mild arm paresis who receive the Arm Ability Training. Restor Neurol Neurosci 2002;20:21-35.
