- Email: [email protected]
Motor imagery based brain-computer interface control of continuous passive motion for wrist extension recovery in chronic stroke patients
Journal Pre-proof Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients Rong-Rong Lu, Mou-Xiong Zheng, Jie Li, Tian-Hao Gao, Xu-Yun Hua, Gang Liu, Song-Hua Huang, Jian-Guang Xu, Yi Wu
PII:
S0304-3940(19)30830-4
DOI:
https://doi.org/10.1016/j.neulet.2019.134727
Reference:
NSL 134727
To appear in:
Neuroscience Letters
Received Date:
22 July 2019
Revised Date:
13 December 2019
Accepted Date:
26 December 2019
Please cite this article as: Lu R-Rong, Zheng M-Xiong, Li J, Gao T-Hao, Hua X-Yun, Liu G, Huang S-Hua, Xu J-Guang, Wu Y, Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients, Neuroscience Letters (2019), doi: https://doi.org/10.1016/j.neulet.2019.134727
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Title Page Title: Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients
Author Names:
ro of
Rong-Rong Lu, M.D.1#, Mou-Xiong Zheng, M.D., Ph.D.3*, Jie Li, M.D., Ph.D.4#, Tian-Hao Gao, B.A.1#, Xu-Yun Hua, M.D., Ph.D.3, Gang Liu, M.D.1, Song-Hua
-p
Huang, B.A.1, Jian-Guang Xu, M.D., Ph.D.2*, Yi Wu, M.D., Ph.D.1*
re
Affiliations:
1. Department of Rehabilitation, Huashan Hospital, Fudan University, No. 12
lP
Middle Wulumuqi Road, Shanghai 200040, China
na
2. School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
ur
3. Department of Traumatology and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
Jo
4. Department of Computer Science and Technology, Tongji University, No. 4800 Cao’an Highway, Shanghai 200092, China
#These authors contributed equally to this work #Corresponding authors 1
Corresponding Authors: Yi Wu, M.D., Ph.D. Address: Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai 200040, China Email: [email protected]
ro of
Tel: 86-21-52887820
Jian-Guang Xu, M.D., Ph.D.
Chinese Medicine, Shanghai 201203, China
re
Email: [email protected]
-p
Address: School of Rehabilitation Science, Shanghai University of Traditional
Jo
ur
na
lP
Tel: 86-21-51322091
2
-p
ro of
Graphical abstract
re
Highlights
Twenty-one patients successfully recovered active wrist extension.
Motor imagery based BCI control of wrist CPM training was applied.
Typical spatial and spectrum patterns of ERD/ERS formed after training.
na
lP
Jo
ur
Abstract Motor recovery of wrist and fingers is still a great challenge for chronic stroke survivors. The present study aimed to verify the efficiency of motor imagery based brain-computer interface (BCI) control of continuous passive motion (CPM) in the recovery of wrist extension due to stroke. An observational study was conducted in 26 chronic stroke patients, aged 49.0±15.4 years, with upper extremity motor impairment. All patients showed no wrist extension recovery. A 24-channel highresolution electroencephalogram (EEG) system was used to acquire cortical signal while they were imagining extension of the affected wrist. Then, 20 sessions of BCI-driven CPM training were carried out for 6 weeks. Primary outcome was the increase of active range of motion (ROM) of the affected wrist from the baseline to final evaluation. Improvement of modified Barthel Index, EEG classification and motor imagery pattern of wrist extension were recorded as secondary outcomes. Twenty-one patients finally passed the EEG screening and completed all the BCI-driven CPM trainings. From baseline to the final evaluation, the increase of active ROM of 3
the affected wrists was (24.05±14.46)˚. The increase of modified Barthel Index was 3.10±4.02 points. But no statistical difference was detected between the baseline and final evaluations (P>0.05). Both EEG classification and motor imagery pattern improved. The present study demonstrated beneficial outcomes of MI-based BCI control of CPM training in motor recovery of wrist extension using motor imagery signal of brain in chronic stroke patients.
Key Words Stroke; rehabilitation; brain-computer interface; continuous passive motion; motor
ro of
imagery
Introduction
Stroke may cause severe impairment to a variety of functions, among which
-p
chronic motor disability is one of the greatest challenges [1]. Rehabilitation can improve the motor function in chronic stroke patients. It has been approved by several
re
randomized controlled trials that intensive training of important motor tasks is
lP
substantially beneficial for motor recovery of upper extremity [2-4]. Many patients are able to regain the control of shoulders and elbows [5]. Unfortunately, motor
na
dysfunctions of wrist and fingers usually persist throughout their life and hard to correct. How to regain the motor control of paralyzed wrist and fingers is still a
ur
concern for physiatrists and therapists.
Jo
Motor imagery (MI) is a dynamic state during which an individual mentally simulates a given action. This type of phenomenal experience implies that the patients feel themselves performing the action [6,7]. This is applicable because even severely disabled patients are still able to imagine movements of the paralyzed wrist and fingers [8,9]. Mental practice of MI is effective when combined with conventional physical therapy for rehabilitation of both upper and lower extremities, which is 4
important for the daily activities and skills [10]. New techniques such as brain-computer interface (BCI) might offer promising recovery of motor function for stroke survivors [11-14]. A BCI is a form of human-computer interface technology that
enables
control
of
a
computer
and
outer
devices
by
modulating
neuro-physiological processes [15]. One of the goals of BCI is to provide motor recovery for paralyzed extremities by utilizing neural signals from the brain [16].
ro of
Electroencephalography (EEG) signal is a common source for BCI since it is non-invasive and affordable and its electrical potential varied among different areas
from the scalp [17]. The most commonly used BCI system was based on event-related
-p
desynchronization (ERD) and even-related synchronization (ERS) phenomena.
re
Feedback-regulated motor imagery could be used to enhance antagonistic ERD/ERS patterns and therewith, support activation of the stroke affected and inhibition of the
lP
non-affected, contralesional hemisphere [18]. And research has demonstrated that BCI
na
technology in assisting MI practice demonstrates the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in subacute stroke
ur
patients with severe motor impairments [19]. Also, visual input played an important part in BCI related training [20]. Virtual-reality (VR) is also commonly used in BCI
Jo
training for stroke patients [21]. However, many patients complained of dizziness and uncomfortableness while wearing VR glasses. Therefore, screen-display feedback was used in the present study. In previous studies, BCI was used as a media to facilitate motor recovery of extremities [22]. In many of these studies, functional electrical stimulation was used 5
as the terminal-effector [23-25]. Some focused on the functional recovery of shoulders and elbows [24], while others on lower extremities [12]. Severe paralysis of wrist and fingers is still a great challenge in clinical practice. For chronic stroke patients, especially, only minor additional recovery could be regained 6 months post-onset [25]. In the present study, continuous passive motion (CPM) was used as the terminal
ro of
effector to provide constant wrist training. And MI based BCI was combined with the
wrist CPM to initiate the robot-assisted physical practice of wrist as feedback. McCabe et al. has compared the effects of wrist CPM and functional electrical
-p
stimulation [26]. Severe stroke survivors with persistent (>1 year) upper-extremity
re
dysfunction improved significantly in coordination and functional task performance in response to both CPM and functional electrical stimulation and no significant
lP
difference was found between these two methods.
na
In the present study, chronic stroke patients with no visible recovery of wrist extension after adequate traditional rehabilitation therapy were recruited for the
ur
investigation of MI based BCI with wrist CPM. Meanwhile, the EEG control ability and EEG pattern pre- and post-intervention were also compared.
Jo
Methods
Participants
Twenty-six chronic stroke patients were recruited in this study. The inclusion criteria were as follows: (i) unilateral brain lesion due to first-ever ischemic or hemorrhagic stroke; (ii) disease duration more than 6 months; (iii) being aged 6
between 16 and 70 years; (iv) being able to operate MI-based BCI by imagining the movement of the paretic wrist (assessed by the EEG analysis); (v) the score of Mini-Mental State Exam (MMSE) were 27 or above; (vi) no visible active wrist extension. The protocol was conducted in accordance with Helsinki Declaration and approved by the local ethical committee of Huashan Hospital. All the participants
ro of
provided written informed consent prior to recruitment. The Chinese Clinical Trial Registry number was ChiCTR-ONC-17010739. EEG Screening
-p
EEG screening was performed to ensure a consistent control signal. A 24-Channel
re
high-resolution EEG system (NGERP-P, NCC Medical Co., LTD, Shanghai, China) was used and electrodes were attached to the scalp according to the 10-20
lP
international system standard as follows: 'F3', 'F4', 'FC3', 'FC4', 'C3', 'C4', 'CP3', 'CP4',
na
'P3', 'P4', 'FT7', 'FT8', 'T3', 'T4', 'TP7', 'TP8', 'Fz', 'Oz', 'FCz', 'Cz', 'CPz', and 'Pz'. The ground electrodes were placed on medial frontal cortex. The reference electrodes were
ur
respectively fixed at left and right mastoids and the average value from bilateral electrodes was used as the reference. EEG signals were collected with the sampling
Jo
rate of 256 Hz.
Before EEG screening, a 2-day practice of wrist extension MI was conducted in
each patient to ensure a full understanding of the tasks. In the data collection process of EEG screening, patients were seated in a comfortable chair with their arms supported and relaxed on the armrests. EEG signals were collected while the patient 7
was performing visually cued MI tasks. Each patient completed 4 separate 10-min sessions to assess the stability of potential BCI control signals, with 2-min intervals of rest. The BCI control signal stability was assessed by calculating the accuracies of classifying rest and motor imagery tasks. For patients with right extremity paralyzed, symbols of “↑→” and “↓→” indicated extension and flexion of the right wrist, respectively. For patients with left extremity paralyzed, symbols of “↑←” and “↓
ro of
←” indicated extension and flexion of the left wrist, respectively. And “×” indicated cease of imagining.
Patients were admitted in the study if the cross-validation accuracy of EEG signal
-p
patterns between rest and MI tasks arrived 57% and above. On the other hand,
re
patients without consistent EEG signal patterns were excluded. BCI Based Training
lP
After EEG screening, the patients completed baseline clinical assessments by a
na
blind physician. Based on the literature and our preliminary research [22-24], patients received a 6-week training that included 20 sessions in total. Each training session
ur
consisted of two parts, EEG-supervised training and BCI-driven CPM training. In the EEG-supervised training, the MI state of wrist extension was tested [19]. In
Jo
this 10-min training, one of the “↑”, “↓” and “×” symbols would be randomly displayed on the screen to cue MI tasks of wrist extension, wrist flexion or cease of imagining, respectively. Patients would stop training if the accuracy of EEG classification was less than 57%. The BCI-driven CPM training consisted of four separate 10-min trials. Before 8
started, the patient’s affected upper extremity was fit on a CPM machine. The ROM of wrist was set from 30° flexion to 40° extension. In each trial of BCI-driven CPM training, the patient was visually cued to imagine wrist extension or flexion by the arrows displayed on a screen (Fig. 1B). If a correct EEG pattern was detected, a cartoon of smiling face would be shown on the screen and the wrist CPM would move toward corresponding direction. If the EEG signal
ro of
was incorrect, a sad face would be shown and the CPM would not move. Then a new cue followed. In this way, both visual and physical feedbacks were provided. If the
patient failed three consecutive times, the CPM would automatically move toward the
-p
direction indicated by the current arrow to enhance their willingness of participation.
re
The success rate of each session was also recorded for statistical analysis. Twenty-six chronic stroke patients participated in the EEG screening and 21
lP
patients (80.8%) completed the study finally (Table 1). Four of the five failed patients
na
(Patients #3, #4, #7, and #22) were unsuitable for further MI-based BCI therapy (three male and one female, all suffered from cerebral infarction), and the rest one (Patient
ur
#23) dropped off because of non-medical reasons. Clinical Outcome Measures
Jo
Primary outcome measure was the increase of active ROM of the affected wrist
from baseline to the end of the 6-week training. Secondary outcome measures included: (1) modified Barthel Index (modified BI); (2) EEG classification accuracy; (3) MI-induced EEG pattern of wrist extension. EEG Classification 9
According to previous researches, the typical spectrum feature of EEG in MI tasks included α (8-13 Hz) and β (14-30 Hz) rhythms [27,28]. Specifically, imagery of hand movement would induce event-related desynchronization (ERD) of 8-30 Hz band in the contralateral hemisphere, and even-related synchronization (ERS) in the ipsilateral hemisphere. In order to acquire MI related cortical activities, the raw EEG signal were
ro of
preprocessed by a band filter of 8-30 Hz and then segmented into several epochs (-1.0-2.5 s, locking to the symbols display event). Power spectrum density (PSD),
which was calculated by fast Fourier transforming, was used to extract the activities
-p
of α and β rhythms.
re
Statistical Analysis
SPSS (version 20.0, Chicago, IL, USA) was used to perform all statistical
lP
analyses in this study. Paired sample T test was used to compare the EEG
na
classification rate. Two sample Mann-Whitney U Test was used to determine difference between groups. Differences were considered statistically significant if p <
ur
0.05. Results
Jo
EEG Classification Rate
In the EEG screening, the average EEG classification rate was 75.37±9.12%
(M±SD). In the EEG-supervised trainings, the EEG classification rates (M±SD) were 64.17±16.49%, 65.95±17.58%, and 72.61±15.68% in Session #1, #10 and #20, respectively. There was no significant difference between any 2 of these 3 sessions 10
(#1 vs #10, p = 0.606; #1 vs #20, p = 0.055; #10 vs #20, p = 0.057). Clinical Outcomes Primary Outcome All participants were not able to actively extend their affected wrists at baseline. Among the 21 patients who completed all the training sessions, 17 (81.0%) regained a certain degree of active wrist extension while the rest four patients (Patients #2, #5, #9
ro of
and #16) failed (Table 1). The increase of active ROM (M±SD) of the affected wrists from baseline to the end of the 6-week training was 24.05±14.46˚.
In further analysis, the increase of active flexion and extension of the affected
-p
wrists were 2.38±3.01˚ and 21.19±14.04˚, respectively.
re
Secondary Outcomes Modified Barthel Index
lP
The increase of modified BI from baseline to the end of the 6-week training was
na
3.10±4.02 points in the 21 patients who completed all the trainings. There was no statistical difference between baseline and final evaluations (p > 0.05).
ur
EEG Pattern
Before the start of EEG screening, no obvious pattern of EEG signal was detected
Jo
while imagining wrist flexion/extension in all the 26 recruited patients. At the end of the 6-week training, however, 11 patients (42.3%) showed characteristic spatial and spectral pattern of ERD/ERS during MI tasks. In the trainings, 3 patients showed success rates of higher than 80%, who also regained satisfying recovery of wrist extension. 11
In order to obtain typical ERD/ERS patterns, the change of power between rest and MI tasks in bilateral hemispheric channels were normalized by subtracting the value of the central channels. Fig. 2 illustrated a typical activation pattern of brain in Patient #6. In this patient, the EEG signals were getting close to a standard ERD/ERS pattern during the first 10 training sessions. From the 15th to 20th sessions, the MI related EEG activity showed more discriminable patterns. When the patient attempted
ro of
to imagine the movement of right (affected) wrist, significant increase in power (i.e., ERD) was recorded from the left hemisphere, while decrease in power (i.e., ERS) from the right. Those signals mostly appeared in the centro-parietal cortical region
-p
(C3 and C4). In two patients, the EEG patterns showed most evident ERD/ERS
re
patterns at channels P3 and P4, instead of C3 and C4 (Fig. 3). Discussion
lP
There have been accumulating researches on the rehabilitation following stroke.
na
But the recovery of wrist and hand is still a great challenge, especially in the chronic stage. In the present study, chronic stroke patients who underwent rehabilitation for
ur
more than 6 months received the MI based BCI control of CPM training. After intensive conventional rehabilitation, the recovery of the shoulder and elbow mobility
Jo
was satisfying. However, none of them regained functional recovery in wrists. At baseline, some of them could flex their wrists but none were able to perform wrist extension. Brain-computer interface is a new interdisciplinary technology and has made rapid progresses recent years. Researchers have integrated BCI into rehabilitation therapy 12
for the treatment of various diseases (e.g., locked-in syndrome, spinal cord injury, stroke) [29-32]. These are obstacles in popularizing this new technology in clinical practice. In many BCI control programs, the protocol should be adjusted according to the patient’s daily imagery performance and assistance of programing specialists was essential. In some researches, a craniotomy surgery was even needed to directly implant electrodes onto the cerebral cortex.
ro of
What we used in the present study was a MI based BCI control of wrist CPM. It
met the requirements of effective BCI practices proposed by Wolpaw et al. [33]. Compared with the BCI system used in the laboratories, it has the advantage of short
-p
set-up time and friendly human-computer interface to physiatrists and therapists who
re
are not familiar with programming. This was quite important in clinical practice. It would impractical if patients need to wait for hours of preparation and another hour of
lP
challenging training. This will make them fatigue and according to our experience, it
na
will also negatively affect their performance in the following training. We chose wrist CPM as a terminal effector for BCI training for two reasons.
ur
Firstly, wrist extension is an essential component in the motion of “reaching” objects, which is important in stroke patients’ everyday uses. But the recovery of wrist
Jo
extension is usually unsatisfactory. Therefore, we chose wrist extension as the target function to treat. Secondly, sensory and motor functions are mutually dependent. Proprioception sensation provided an essential feedback in the recovery of motor function [34-35]. By using wrist CPM, the position sensation of the affected wrist was enhanced. Also, other study using robotic orthoses as terminal effector indicated that 13
it could promote long-lasting improvements in motor function of chronic stroke patients with severe paresis [36]. The present study revealed the feasibility of MI based BCI control of CPM training in chronic stroke patients with impairment of wrist extension. Among the participants who completed all the trainings, 80% showed apparent improvement of active wrist extension. Some patients could even extend 1 or 2 fingers while the flexor
ro of
muscles in the forearm were relaxed. Although this motor recovery was marginal and mostly non-functional, it still greatly encouraged these patients. The process of recovery from stroke-induced hemiplegia usually follows a relatively predictable,
-p
stereotyped series of events. Most recovery takes place within the first 3 months and
re
only minor additional recovery occurs beyond 6 months post-onset [23]. According to Twitchell et al.’s research, if stroke patients show no measurable grasp strength by 4
lP
weeks and prolonged flaccidity period, they may have poor prognosis of the hand [32].
na
Therefore, for our patients who showed little recovery of wrist and fingers after the initial post-onset stage, the outcomes of wrist extension recovery were meaningful.
ur
Even though, the improvement of modified BI was not obvious. There might be explanations. Firstly, the patients in our study were all chronic stroke survivors and
Jo
had underwent adequate conventional rehabilitation therapy for months or even years. In the acute stage, they might depend on the caregivers. But the goal of rehabilitation was to improve their independence and help them return to the society [26]. Most of the chronic stroke survivors could take good care of themselves regardless of the severity of motor impairment. Secondly, the present training aimed at improving the 14
motor function of affected wrists. Isolated improvement of this single movement might be too subtle to substantially influence the whole functional activity. An integration of several rehabilitation therapies might still be needed to increase the modified BI score. According to both literature and our previous studies of healthy volunteers, imagery of hand movement would induce ERD from contralateral hemisphere and
ro of
ERS from ipsilateral hemisphere [28,38-40]. This phenomenon mostly occurs in the centro-parietal region, and especially evident in channels C3 and C4. However, EEG activities usually vary among stroke patients. The location and severity of the brain
-p
lesion would also influence the extent of reorganization of the neural motor network
re
[41]. Therefore, the patients’ brain activation pattern during MI would be different from healthy volunteers. In the present study, the PSD features involving α (8-13Hz)
lP
and β (14-30Hz) rhythms were analyzed. It was found that typical ERD/ERS patterns
na
appeared in most patients as healthy volunteers after practice. However, two patients’ EEG patterns showed most evident ERD/ERS patterns around channels of P3 and P4,
ur
instead of C3 and C4. It might be due to a reorganized neural motor network in a lesioned brain.
Jo
The reason why only chronic stroke patients were included in the present study
was to minimize the potential influence of spontaneous recovery on the final outcomes. As the prognosis tended to be poor in chronic stroke patients with severe motor dysfunction, the potential of spontaneous recovery was minimal in our patients. Given that no further improvement was expected following long-term traditional 15
rehabilitation therapy in these patients, we considered it still be feasible to compare pre- and post-treatment evaluations. Even though, we believe that acute and subacute stroke patients could also benefit from this treatment. Further researches are still needed to verify this hypothesis. Conclusions In the present study, motor imagery based brain-computer interface in the control
ro of
of wrist continuous passive motion was applied in chronic stroke patients. The results
indicated that patients’ active wrist extension function improved after 6 weeks’ training. Meanwhile, the spatial and spectrum pattern of EEG signal also improved
-p
subsequent to the training. The study indicated that motor imagery based
re
brain-computer interface control of continuous passive motion might be a potentially effective therapy for chronic stroke patients.
lP
Limitations
na
There are still limitations in this study. Firstly, it was only a preliminary study in a series of chronic stroke patients. Although pre- and post-treatment comparison was
ur
acceptable in these patients, (an) adequate control group(s) might still be needed to acquire better evidences. Secondly, improvement in the wrist extension was still
Jo
insufficient for functional uses in daily life. This treatment may not substitute the whole traditional rehabilitation therapy. Moreover, studies including motor evoked potential (MEP) and DTI may further our underlying of brain reorganization mechanisms underlying this treatment.
16
Author Contributions Section Y.W. and J.X. designed the study and monitored the progress, data collection, and analysis. M.Z. and X.H. interpreted the data and made critical revision of the paper. R.L., T.G., G.L., and S.H. designed the study and collected and analyzed the data. J.L. collected the EEG data.
Acknowledgements This work was supported by Shanghai Sailing Program [grant number
ro of
16YF1415300]; Medical Research Project in Jing’an District [grant number 2015QN06]; Shanghai Rising-Star Program [grant number 19QA1409000]; Shanghai
Municipal Commission of Health and Family Planning [grant number 2018YQ02];
-p
Shanghai Commission of Science and Technology [grant number 18441903800];
re
Shanghai "Rising Stars of Medical Talent" Youth Development Program [grant
Jo
ur
na
2018YFC2001600].
lP
number RY411.19.01.10]; National Key R&D Program of China [grant number
17
References: [1] P. Langhorne, F. Coupar, A. Pollock. Motor recovery after stroke: a systematic review. Lancet Neurol 8 (2009) 741-754. [2] V.W. Mark, E. Taub, D.M. Morris. Neuroplasticity and constraint-induced movement therapy. Eura Medicophys 42 (2006) 269-284. [3] E. Taub, G. Uswatte, D.K. King, D. Morris, J.E. Crago, A. Chatterjee. A placebo-controlled trial of constraint-induced movement therapy for upper extremity after stroke. Stroke 37 (2006) 1045-1049. [4] K.C. Lin, H.Y. Chung, C.Y. Wu, H.L. Liu, Y.W. Hsieh, I.H. Chen, C.L. Chen, L.L. Chuang, J.S. Liu, Y.Y. Wai. Constraint-induced therapy versus control intervention in patients with stroke: a functional magnetic resonance imaging study. Am J Phys Med Rehabil 89 (2010) 177-185.
ro of
[5] P. Langhorne, J. Bernhardt, G. Kwakkel. Stroke rehabilitation. Lancet 377 (2011) 1693-1702.
[6] J. Decety. Do imagined and executed actions share the same neural substrate? Brain Res Cogn Brain Res 3 (1996) 87-93.
[7] S. Braun, M. Kleynen, J. Schols, T. Schack, A. Beurskens, D. Wade. Using mental practice in stroke rehabilitation: a framework. Clin Rehabil 22 (2008) 579.
-p
[8] M. Ietswaart, M. Johnston, H.C. Dijkerman, S. Joice, C.L. Scott, R.S. MacWalter, S.J. Hamilton.
Mental practice with motor imagery in stroke recovery: randomized controlled trial of efficacy. Brain 134 (2011) 1373-1386.
re
[9] J. Harris, A. Hebert. Utilization of motor imagery in upper limb rehabilitation: A systematic scoping review. Clin Rehabil 29 (2015) 1625481624.
[10] C.D. Garcia, C.J. Aboitiz. Effectiveness of motor imagery or mental practice in functional
lP
recovery after stroke: a systematic review. Neurologia 31 (2016) 43-52. [11] K.K. Ang, K.S. Chua, K.S. Phua, C. Wang, Z.Y. Chin, C.W. Kuah, W. Low, C. Guan. A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke. Clin Eeg Neurosci 46 (2015) 310-320.
na
[12] N. Mrachacz-Kersting, N. Jiang, A.J. Stevenson, I.K. Niazi, V. Kostic, A. Pavlovic, S. Radovanovic, M. Djuric-Jovicic, F. Agosta, K. Dremstrup, D. Farina. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J Neurophysiol 115 (2016) 1410-1421.
ur
[13] L.E.H.V. Dokkum, T. Ward, I. Laffont. Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke. Ann Phys Rehabil Med 58 (2015) 3-8.
Jo
[14] D.T. Bundy, L. Souders, K. Baranyai, L. Leonard, G. Schalk, R. Coker, D.W. Moran, T. Huskey, E.C. Leuthardt. Contralesional Brain–Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors. Stroke 48 (2017) 1908-1915.
[15] S. Blain-Moraes, R. Schaff, K.L. Gruis, J.E. Huggins, P.A. Wren. Barriers to and mediators of brain-computer interface user acceptance: focus group findings. Ergonomics 55 (2012) 516-525.
[16] J.L. Collinger, M.L. Boninger, T.M. Bruns, K. Curley, W. Wang, D.J. Weber. Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. J Rehabil Res Dev 50 (2013) 145-160. [17] Prasad G, Herman P, Coyle D, McDonough S, Crosbie J. Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. 18
J Neuroeng Rehabil. 2010;7:60. [18] 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. [19] Pichiorri F, Morone G, Petti M, Toppi J, Pisotta I, Molinari M, Paolucci S, Inghilleri M, Astolfi L, Cincotti F, Mattia D. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015;77(5):851-65. [20] Vourvopoulos A, Jorge C, Abreu R, Figueiredo P, Fernandes JC, Bermúdez I Badia S. Efficacy and Brain Imaging Correl ates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report. Front Hum Neurosci. 2019;13:244. [21] Saposnik G, Cohen LG, Mamdani M, Pooyania S, Ploughman M, Cheung D, Shaw J, Hall J, Nord P, Dukelow S, Nilanont Y, De Los Rios F, Olmos L, Levin M, Teasell R, Cohen A, Thorpe K, Laupacis A, Bayley M; Stroke Outcomes Research Canada. Efficacy and safety of non-immersive virtual reality exercising in stroke rehabilitation (EVREST): a randomised, multicentre,
ro of
single-blind, controlled trial. Lancet Neurol. 2016;15(10):1019-1027.
[22] C.M. McCrimmon, C.E. King, P.T. Wang, S.C. Cramer, Z. Nenadic, A.H. Do. Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J Neuroeng Rehabil 12 (2015) 57.
[23] E. Chung, J.H. Kim, D.S. Park, B.H. Lee. Effects of brain-computer interface-based functional
-p
electrical stimulation on brain activation in stroke patients: a pilot randomized controlled trial. J Phys Ther Sci 27 (2015) 559-562.
[24] Y. Jang, T. Kim, B. Lee. Effects of brain-computer interface-controlled functional electrical Occup Ther Int 23 (2016) 175-185.
re
stimulation training on shoulder subluxation for patients with stroke: a randomized controlled trial. [25] S.J. Cuccurullo.: Physical Medicine and Rehabilitation Board Review (3rd Ed), Demos Medical
lP
Publishing, New York, 2014.
[26] J. McCabe, M. Monkiewicz, J. Holcomb, S. Pundik, J.J. Daly. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity 981-990.
na
dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil 96 (2015) [27] G. Pfurtscheller, C. Neuper. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239 (1997) 65-68.
ur
[28] H. Ramoser, J. Müller-Gerking, G. Pfurtscheller. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8 (2000) 441. [29] G.R. Müller-Putz, R. Scherer, G. Pfurtscheller, R. Rupp. EEG-based neuroprosthesis control: A
Jo
step towards clinical practice. Neurosci Lett 382 (2005) 169-174. [30] A. Kbler, F. Nijboer, J. Mellinger, T. Vaughan, H. Pawelzik, G. Schalk, D. McFarland, N. Birbaumer, J. Wolpaw. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64 (2005) 1775-1777.
[31] C. Enzinger, S. Ropele, F. Fazekas, M. Loitfelder, F. Gorani, T. Seifert, G. Reiter, C. Neuper, G. Pfurtscheller, G. Muller-Putz. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Exp Brain Res 190 (2008) 215-223. [32] E. Buch, C. Weber, L.G. Cohen, C. Braun, N. Birbaumer. Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39 (2008) 910. [33] J.R. Wolpaw, N. Birbaumer, W.J. Heetderks, D.J. McFarland, P.H. Peckham, G. Schalk, E. 19
Donchin, L.A. Quatrano, C.J. Robinson, T.M. Vaughan. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8 (2000) 164. [34] Aman JE, Elangovan N, Yeh IL, Konczak J. The effectiveness of proprioceptive training for improving motor function: a systematic review. Front Hum Neurosci. 2015;8:1075. [35] Edwards LL, King EM, Buetefisch CM, Borich MR. Putting the "Sensory" Into Sensorimotor Control: The Role of Sensorimotor Integration in Goal-Directed Hand Movements After Stroke. Front Integr Neurosci. 2019;13:16. [36] Ramos-Murguialday A, Curado MR, Broetz D, Yilmaz Ö, Brasil FL, Liberati G, Garcia-Cossio E, Cho W, Caria A, Cohen LG, Birbaumer N. Brain-machine interface in chronic stroke: randomized trial long-term follow-up. Neurorehabil Neural Repair. 2019;33(3):188-198. [37] T.E. TWITCHELL. The restoration of motor function following hemiplegia in man. Brain 74 (1951) 443-480. [38] J. Li, H. Ji, L. Cao, D. Zang, R. Gu, B. Xia, Q. Wu. Evaluation and application of a hybrid brain
ro of
computer interface for real wheelchair parallel control with multi-degree of freedom. Int J Neural Syst 24 (2014) 1450014.
[39] H. Ji, J. Li, R. Lu, R. Gu, L. Cao, X. Gong. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme. Comput Intell Neurosci 2016 (2016) 1732836.
-p
[40] J. Li, L. Zhang, D. Tao, H. Sun, Q. Zhao. A prior neurophysiologic knowledge free tensor-based scheme for single trial EEG classification. IEEE Trans Neural Syst Rehabil Eng 17 (2009) 107-115.
re
[41] C.H. Park, W.H. Chang, S.H. Ohn, S.T. Kim, O.Y. Bang, A. Pascual-Leone, Y.H. Kim. Longitudinal changes of resting-state functional connectivity during motor recovery after stroke.
Jo
ur
na
lP
Stroke 42 (2011) 1357-1362.
20
Figure Legends Figure 1. Diagram of study design. (A) Flow chart of the study protocol. Patients who meet the inclusion criteria were recruited for 2-day practice of wrist extension motor imagery (MI) and then underwent four sessions of electroencephalographic (EEG) screening. Those who showed consistent MI related EEG pattern received baseline clinical assessments and completed a 6-week’s brain-computer interface (BCI)-driven continuous passive motion (CPM) training, followed by the
ro of
final clinical assessment. (B) The process of BCI-driven CPM training. The patient was wearing an
EEG cap of 24-channel high-resolution EEG system. His affected forearm and hand were attached to a
Jo
ur
na
lP
re
performing the visually cued MI tasks of wrist extension.
-p
CPM machine with straps. The CPM was controlled by the EEG signal while the patient was
21
Figure 2. Exemplar spatial and spectrum pattern of electroencephalographic (EEG). The figures showed the EEG mapping of an exemplar patient (Patient #6) during the 20-session brain-computer interface (BCI)-driven continuous passive motion (CPM) training. The patient tended to show a typical event-related desynchronization/event-related synchronization (ERD/ERS) pattern in the 8-30 Hz band power. When he imagined the movement of right (affected) wrist, significant increase in power (i.e., ERD) was recorded from the left (lesioned) hemisphere, while decrease in power (i.e., ERS) from the
Jo
ur
na
lP
re
-p
ro of
right. (L: left; R: right)
22
Figure 3. The time-frequency plots during the motor imagery (MI) task of affected wrist movement. A visualization example of the assembled tensor during the MI task of the right (affected) wrist extension. The spectrograms were shown at each channel according to channels distribution over the scalp, with time ranging from 0 to 4 s and frequency ranging from 8 to 30 Hz. The spectrograms at C3, C4, P3 and P4 channels were amplified at the bottom row. (ERD: event-related desynchronization;
Jo
ur
na
lP
re
-p
ro of
ERS: event-related synchronization)
23
f
No.
Sex
Age (yr)
Diagnosis
Affected UE
Post-stroke duration (mo)
Whether included
1
M
33
Ischemic stroke
R
6
Yes
2
F
58
Hemorrhagic stroke
L
6
3
M
54
Ischemic stroke
L
6
4
F
61
Ischemic stroke
R
12
5
M
64
Ischemic stroke
L
20
6
M
42
Hemorrhagic stroke
R
7
M
32
Ischemic stroke
R
8
M
75
Ischemic stroke
9
M
43
10
M
11
No
NA
pr No
Pr
e-
Yes
Yes Yes
6
No
L
17
Yes
Hemorrhagic stroke
R
13
Yes
32
Hemorrhagic stroke
R
7
Yes
M
56
Ischemic stroke
L
10
Yes
12
M
32
Hemorrhagic stroke
R
9
Yes
13
M
37
Hemorrhagic stroke
L
12
Yes
14
M
63
Ischemic stroke
R
8
Yes
na l
Baseline ROM of affected wrist
10° (10° fle ~ 0° ext) 0° (0° fle ~ 0° ext) NA
31
Jo ur
oo
Table 1. Patient Characteristics
0° (0° fle ~ 0° ext) 10° (10° fle ~ 0° ext) NA 0 ° (0 ° fle ~ 0 ° ext) 0 ° (0 ° fle ~ 0 ° ext) 10° (10° fle ~ 0° ext) 0 ° (0 ° fle ~ 0 ° ext) 10° (10° fle ~ 0° ext) 10° (10° fle ~ 0° ext) 0 ° (0 ° fle ~ 0 °
Completion ROM of affected wrist
Baseline BI
Comple tion BI
45° (15° fle ~ 30° ext)
90
90
0°(0° fle ~ 0° ext)
70
75
NA
NA
NA
NA
NA
NA
0°(0° fle ~ 0° ext)
90
90
35° (10° fle ~ 25° ext)
100
100
NA
NA
NA
15° (10° fle ~ 5° ext)
70
70
0°(0° fle ~ 0° ext)
70
80
45° (15° fle ~ 30° ext)
100
100
20° (5° fle ~ 15° ext)
60
65
45° (10° fle ~ 35° ext)
100
100
40° (10° fle ~ 30° ext)
100
100
25° (5° fle ~ 20° ext)
90
95
24
M
32
Hemorrhagic stroke
R
11
Yes
16
F
64
Ischemic stroke
L
11
Yes
17
M
63
Ischemic stroke
R
11
18
M
37
Hemorrhagic stroke
L
17
19
M
44
Hemorrhagic stroke
R
20
M
63
Ischemic stroke
R
21
M
43
Ischemic stroke
22
M
77
23
M
24
f
10° (10° fle ~ 0° ext)
0 ° (0 ° fle ~ 0 °
pr
15
oo
ext)
Pr
e-
Yes Yes
ext)
10° (10° fle ~ 0° ext)
10° (10° fle ~ 0° ext) 0 ° (0 ° fle ~ 0 °
50° (15° fle ~ 35° ext)
100
100
5°(5° fle ~ 0° ext)
50
60
40° (10° fle ~ 30° ext)
90
90
45° (10° fle ~ 35° ext)
100
100
20° (5° fle ~ 15° ext)
70
80
50° (10° fle ~ 40° ext)
90
90
15° (5° fle ~ 10° ext)
95
100
Yes
12
Yes
L
6
Yes
Ischemic stroke
L
8
No
NA
NA
NA
NA
56
Ischemic stroke
L
6
No
NA
NA
NA
NA
M
50
Hemorrhagic stroke
L
43
Yes
40° (10° fle ~ 30° ext)
60
70
25
M
17
Hemorrhagic stroke
R
19
Yes
25° (5° fle ~ 20° ext)
100
100
26
M
47
Hemorrhagic stroke
R
23
Yes
55° (15° fle ~ 40° ext)
55
60
Jo ur
na l
6
ext) 10° (10° fle ~ 0° ext) 5 ° (5 ° fle ~ 0 ° ext)
10° (10° fle ~ 0° ext) 5 ° (5 ° fle ~ 0 ° ext) 10° (10° fle ~ 0° ext)
M: male; L: left; R: right; UE: upper extremity; yr: year; mo: month; ROM: range of motion; BI: Barthel index; fle: flexion; ext: extension; NA: not applicable
25
PII:
S0304-3940(19)30830-4
DOI:
https://doi.org/10.1016/j.neulet.2019.134727
Reference:
NSL 134727
To appear in:
Neuroscience Letters
Received Date:
22 July 2019
Revised Date:
13 December 2019
Accepted Date:
26 December 2019
Please cite this article as: Lu R-Rong, Zheng M-Xiong, Li J, Gao T-Hao, Hua X-Yun, Liu G, Huang S-Hua, Xu J-Guang, Wu Y, Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients, Neuroscience Letters (2019), doi: https://doi.org/10.1016/j.neulet.2019.134727
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Title Page Title: Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients
Author Names:
ro of
Rong-Rong Lu, M.D.1#, Mou-Xiong Zheng, M.D., Ph.D.3*, Jie Li, M.D., Ph.D.4#, Tian-Hao Gao, B.A.1#, Xu-Yun Hua, M.D., Ph.D.3, Gang Liu, M.D.1, Song-Hua
-p
Huang, B.A.1, Jian-Guang Xu, M.D., Ph.D.2*, Yi Wu, M.D., Ph.D.1*
re
Affiliations:
1. Department of Rehabilitation, Huashan Hospital, Fudan University, No. 12
lP
Middle Wulumuqi Road, Shanghai 200040, China
na
2. School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
ur
3. Department of Traumatology and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
Jo
4. Department of Computer Science and Technology, Tongji University, No. 4800 Cao’an Highway, Shanghai 200092, China
#These authors contributed equally to this work #Corresponding authors 1
Corresponding Authors: Yi Wu, M.D., Ph.D. Address: Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai 200040, China Email: [email protected]
ro of
Tel: 86-21-52887820
Jian-Guang Xu, M.D., Ph.D.
Chinese Medicine, Shanghai 201203, China
re
Email: [email protected]
-p
Address: School of Rehabilitation Science, Shanghai University of Traditional
Jo
ur
na
lP
Tel: 86-21-51322091
2
-p
ro of
Graphical abstract
re
Highlights
Twenty-one patients successfully recovered active wrist extension.
Motor imagery based BCI control of wrist CPM training was applied.
Typical spatial and spectrum patterns of ERD/ERS formed after training.
na
lP
Jo
ur
Abstract Motor recovery of wrist and fingers is still a great challenge for chronic stroke survivors. The present study aimed to verify the efficiency of motor imagery based brain-computer interface (BCI) control of continuous passive motion (CPM) in the recovery of wrist extension due to stroke. An observational study was conducted in 26 chronic stroke patients, aged 49.0±15.4 years, with upper extremity motor impairment. All patients showed no wrist extension recovery. A 24-channel highresolution electroencephalogram (EEG) system was used to acquire cortical signal while they were imagining extension of the affected wrist. Then, 20 sessions of BCI-driven CPM training were carried out for 6 weeks. Primary outcome was the increase of active range of motion (ROM) of the affected wrist from the baseline to final evaluation. Improvement of modified Barthel Index, EEG classification and motor imagery pattern of wrist extension were recorded as secondary outcomes. Twenty-one patients finally passed the EEG screening and completed all the BCI-driven CPM trainings. From baseline to the final evaluation, the increase of active ROM of 3
the affected wrists was (24.05±14.46)˚. The increase of modified Barthel Index was 3.10±4.02 points. But no statistical difference was detected between the baseline and final evaluations (P>0.05). Both EEG classification and motor imagery pattern improved. The present study demonstrated beneficial outcomes of MI-based BCI control of CPM training in motor recovery of wrist extension using motor imagery signal of brain in chronic stroke patients.
Key Words Stroke; rehabilitation; brain-computer interface; continuous passive motion; motor
ro of
imagery
Introduction
Stroke may cause severe impairment to a variety of functions, among which
-p
chronic motor disability is one of the greatest challenges [1]. Rehabilitation can improve the motor function in chronic stroke patients. It has been approved by several
re
randomized controlled trials that intensive training of important motor tasks is
lP
substantially beneficial for motor recovery of upper extremity [2-4]. Many patients are able to regain the control of shoulders and elbows [5]. Unfortunately, motor
na
dysfunctions of wrist and fingers usually persist throughout their life and hard to correct. How to regain the motor control of paralyzed wrist and fingers is still a
ur
concern for physiatrists and therapists.
Jo
Motor imagery (MI) is a dynamic state during which an individual mentally simulates a given action. This type of phenomenal experience implies that the patients feel themselves performing the action [6,7]. This is applicable because even severely disabled patients are still able to imagine movements of the paralyzed wrist and fingers [8,9]. Mental practice of MI is effective when combined with conventional physical therapy for rehabilitation of both upper and lower extremities, which is 4
important for the daily activities and skills [10]. New techniques such as brain-computer interface (BCI) might offer promising recovery of motor function for stroke survivors [11-14]. A BCI is a form of human-computer interface technology that
enables
control
of
a
computer
and
outer
devices
by
modulating
neuro-physiological processes [15]. One of the goals of BCI is to provide motor recovery for paralyzed extremities by utilizing neural signals from the brain [16].
ro of
Electroencephalography (EEG) signal is a common source for BCI since it is non-invasive and affordable and its electrical potential varied among different areas
from the scalp [17]. The most commonly used BCI system was based on event-related
-p
desynchronization (ERD) and even-related synchronization (ERS) phenomena.
re
Feedback-regulated motor imagery could be used to enhance antagonistic ERD/ERS patterns and therewith, support activation of the stroke affected and inhibition of the
lP
non-affected, contralesional hemisphere [18]. And research has demonstrated that BCI
na
technology in assisting MI practice demonstrates the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in subacute stroke
ur
patients with severe motor impairments [19]. Also, visual input played an important part in BCI related training [20]. Virtual-reality (VR) is also commonly used in BCI
Jo
training for stroke patients [21]. However, many patients complained of dizziness and uncomfortableness while wearing VR glasses. Therefore, screen-display feedback was used in the present study. In previous studies, BCI was used as a media to facilitate motor recovery of extremities [22]. In many of these studies, functional electrical stimulation was used 5
as the terminal-effector [23-25]. Some focused on the functional recovery of shoulders and elbows [24], while others on lower extremities [12]. Severe paralysis of wrist and fingers is still a great challenge in clinical practice. For chronic stroke patients, especially, only minor additional recovery could be regained 6 months post-onset [25]. In the present study, continuous passive motion (CPM) was used as the terminal
ro of
effector to provide constant wrist training. And MI based BCI was combined with the
wrist CPM to initiate the robot-assisted physical practice of wrist as feedback. McCabe et al. has compared the effects of wrist CPM and functional electrical
-p
stimulation [26]. Severe stroke survivors with persistent (>1 year) upper-extremity
re
dysfunction improved significantly in coordination and functional task performance in response to both CPM and functional electrical stimulation and no significant
lP
difference was found between these two methods.
na
In the present study, chronic stroke patients with no visible recovery of wrist extension after adequate traditional rehabilitation therapy were recruited for the
ur
investigation of MI based BCI with wrist CPM. Meanwhile, the EEG control ability and EEG pattern pre- and post-intervention were also compared.
Jo
Methods
Participants
Twenty-six chronic stroke patients were recruited in this study. The inclusion criteria were as follows: (i) unilateral brain lesion due to first-ever ischemic or hemorrhagic stroke; (ii) disease duration more than 6 months; (iii) being aged 6
between 16 and 70 years; (iv) being able to operate MI-based BCI by imagining the movement of the paretic wrist (assessed by the EEG analysis); (v) the score of Mini-Mental State Exam (MMSE) were 27 or above; (vi) no visible active wrist extension. The protocol was conducted in accordance with Helsinki Declaration and approved by the local ethical committee of Huashan Hospital. All the participants
ro of
provided written informed consent prior to recruitment. The Chinese Clinical Trial Registry number was ChiCTR-ONC-17010739. EEG Screening
-p
EEG screening was performed to ensure a consistent control signal. A 24-Channel
re
high-resolution EEG system (NGERP-P, NCC Medical Co., LTD, Shanghai, China) was used and electrodes were attached to the scalp according to the 10-20
lP
international system standard as follows: 'F3', 'F4', 'FC3', 'FC4', 'C3', 'C4', 'CP3', 'CP4',
na
'P3', 'P4', 'FT7', 'FT8', 'T3', 'T4', 'TP7', 'TP8', 'Fz', 'Oz', 'FCz', 'Cz', 'CPz', and 'Pz'. The ground electrodes were placed on medial frontal cortex. The reference electrodes were
ur
respectively fixed at left and right mastoids and the average value from bilateral electrodes was used as the reference. EEG signals were collected with the sampling
Jo
rate of 256 Hz.
Before EEG screening, a 2-day practice of wrist extension MI was conducted in
each patient to ensure a full understanding of the tasks. In the data collection process of EEG screening, patients were seated in a comfortable chair with their arms supported and relaxed on the armrests. EEG signals were collected while the patient 7
was performing visually cued MI tasks. Each patient completed 4 separate 10-min sessions to assess the stability of potential BCI control signals, with 2-min intervals of rest. The BCI control signal stability was assessed by calculating the accuracies of classifying rest and motor imagery tasks. For patients with right extremity paralyzed, symbols of “↑→” and “↓→” indicated extension and flexion of the right wrist, respectively. For patients with left extremity paralyzed, symbols of “↑←” and “↓
ro of
←” indicated extension and flexion of the left wrist, respectively. And “×” indicated cease of imagining.
Patients were admitted in the study if the cross-validation accuracy of EEG signal
-p
patterns between rest and MI tasks arrived 57% and above. On the other hand,
re
patients without consistent EEG signal patterns were excluded. BCI Based Training
lP
After EEG screening, the patients completed baseline clinical assessments by a
na
blind physician. Based on the literature and our preliminary research [22-24], patients received a 6-week training that included 20 sessions in total. Each training session
ur
consisted of two parts, EEG-supervised training and BCI-driven CPM training. In the EEG-supervised training, the MI state of wrist extension was tested [19]. In
Jo
this 10-min training, one of the “↑”, “↓” and “×” symbols would be randomly displayed on the screen to cue MI tasks of wrist extension, wrist flexion or cease of imagining, respectively. Patients would stop training if the accuracy of EEG classification was less than 57%. The BCI-driven CPM training consisted of four separate 10-min trials. Before 8
started, the patient’s affected upper extremity was fit on a CPM machine. The ROM of wrist was set from 30° flexion to 40° extension. In each trial of BCI-driven CPM training, the patient was visually cued to imagine wrist extension or flexion by the arrows displayed on a screen (Fig. 1B). If a correct EEG pattern was detected, a cartoon of smiling face would be shown on the screen and the wrist CPM would move toward corresponding direction. If the EEG signal
ro of
was incorrect, a sad face would be shown and the CPM would not move. Then a new cue followed. In this way, both visual and physical feedbacks were provided. If the
patient failed three consecutive times, the CPM would automatically move toward the
-p
direction indicated by the current arrow to enhance their willingness of participation.
re
The success rate of each session was also recorded for statistical analysis. Twenty-six chronic stroke patients participated in the EEG screening and 21
lP
patients (80.8%) completed the study finally (Table 1). Four of the five failed patients
na
(Patients #3, #4, #7, and #22) were unsuitable for further MI-based BCI therapy (three male and one female, all suffered from cerebral infarction), and the rest one (Patient
ur
#23) dropped off because of non-medical reasons. Clinical Outcome Measures
Jo
Primary outcome measure was the increase of active ROM of the affected wrist
from baseline to the end of the 6-week training. Secondary outcome measures included: (1) modified Barthel Index (modified BI); (2) EEG classification accuracy; (3) MI-induced EEG pattern of wrist extension. EEG Classification 9
According to previous researches, the typical spectrum feature of EEG in MI tasks included α (8-13 Hz) and β (14-30 Hz) rhythms [27,28]. Specifically, imagery of hand movement would induce event-related desynchronization (ERD) of 8-30 Hz band in the contralateral hemisphere, and even-related synchronization (ERS) in the ipsilateral hemisphere. In order to acquire MI related cortical activities, the raw EEG signal were
ro of
preprocessed by a band filter of 8-30 Hz and then segmented into several epochs (-1.0-2.5 s, locking to the symbols display event). Power spectrum density (PSD),
which was calculated by fast Fourier transforming, was used to extract the activities
-p
of α and β rhythms.
re
Statistical Analysis
SPSS (version 20.0, Chicago, IL, USA) was used to perform all statistical
lP
analyses in this study. Paired sample T test was used to compare the EEG
na
classification rate. Two sample Mann-Whitney U Test was used to determine difference between groups. Differences were considered statistically significant if p <
ur
0.05. Results
Jo
EEG Classification Rate
In the EEG screening, the average EEG classification rate was 75.37±9.12%
(M±SD). In the EEG-supervised trainings, the EEG classification rates (M±SD) were 64.17±16.49%, 65.95±17.58%, and 72.61±15.68% in Session #1, #10 and #20, respectively. There was no significant difference between any 2 of these 3 sessions 10
(#1 vs #10, p = 0.606; #1 vs #20, p = 0.055; #10 vs #20, p = 0.057). Clinical Outcomes Primary Outcome All participants were not able to actively extend their affected wrists at baseline. Among the 21 patients who completed all the training sessions, 17 (81.0%) regained a certain degree of active wrist extension while the rest four patients (Patients #2, #5, #9
ro of
and #16) failed (Table 1). The increase of active ROM (M±SD) of the affected wrists from baseline to the end of the 6-week training was 24.05±14.46˚.
In further analysis, the increase of active flexion and extension of the affected
-p
wrists were 2.38±3.01˚ and 21.19±14.04˚, respectively.
re
Secondary Outcomes Modified Barthel Index
lP
The increase of modified BI from baseline to the end of the 6-week training was
na
3.10±4.02 points in the 21 patients who completed all the trainings. There was no statistical difference between baseline and final evaluations (p > 0.05).
ur
EEG Pattern
Before the start of EEG screening, no obvious pattern of EEG signal was detected
Jo
while imagining wrist flexion/extension in all the 26 recruited patients. At the end of the 6-week training, however, 11 patients (42.3%) showed characteristic spatial and spectral pattern of ERD/ERS during MI tasks. In the trainings, 3 patients showed success rates of higher than 80%, who also regained satisfying recovery of wrist extension. 11
In order to obtain typical ERD/ERS patterns, the change of power between rest and MI tasks in bilateral hemispheric channels were normalized by subtracting the value of the central channels. Fig. 2 illustrated a typical activation pattern of brain in Patient #6. In this patient, the EEG signals were getting close to a standard ERD/ERS pattern during the first 10 training sessions. From the 15th to 20th sessions, the MI related EEG activity showed more discriminable patterns. When the patient attempted
ro of
to imagine the movement of right (affected) wrist, significant increase in power (i.e., ERD) was recorded from the left hemisphere, while decrease in power (i.e., ERS) from the right. Those signals mostly appeared in the centro-parietal cortical region
-p
(C3 and C4). In two patients, the EEG patterns showed most evident ERD/ERS
re
patterns at channels P3 and P4, instead of C3 and C4 (Fig. 3). Discussion
lP
There have been accumulating researches on the rehabilitation following stroke.
na
But the recovery of wrist and hand is still a great challenge, especially in the chronic stage. In the present study, chronic stroke patients who underwent rehabilitation for
ur
more than 6 months received the MI based BCI control of CPM training. After intensive conventional rehabilitation, the recovery of the shoulder and elbow mobility
Jo
was satisfying. However, none of them regained functional recovery in wrists. At baseline, some of them could flex their wrists but none were able to perform wrist extension. Brain-computer interface is a new interdisciplinary technology and has made rapid progresses recent years. Researchers have integrated BCI into rehabilitation therapy 12
for the treatment of various diseases (e.g., locked-in syndrome, spinal cord injury, stroke) [29-32]. These are obstacles in popularizing this new technology in clinical practice. In many BCI control programs, the protocol should be adjusted according to the patient’s daily imagery performance and assistance of programing specialists was essential. In some researches, a craniotomy surgery was even needed to directly implant electrodes onto the cerebral cortex.
ro of
What we used in the present study was a MI based BCI control of wrist CPM. It
met the requirements of effective BCI practices proposed by Wolpaw et al. [33]. Compared with the BCI system used in the laboratories, it has the advantage of short
-p
set-up time and friendly human-computer interface to physiatrists and therapists who
re
are not familiar with programming. This was quite important in clinical practice. It would impractical if patients need to wait for hours of preparation and another hour of
lP
challenging training. This will make them fatigue and according to our experience, it
na
will also negatively affect their performance in the following training. We chose wrist CPM as a terminal effector for BCI training for two reasons.
ur
Firstly, wrist extension is an essential component in the motion of “reaching” objects, which is important in stroke patients’ everyday uses. But the recovery of wrist
Jo
extension is usually unsatisfactory. Therefore, we chose wrist extension as the target function to treat. Secondly, sensory and motor functions are mutually dependent. Proprioception sensation provided an essential feedback in the recovery of motor function [34-35]. By using wrist CPM, the position sensation of the affected wrist was enhanced. Also, other study using robotic orthoses as terminal effector indicated that 13
it could promote long-lasting improvements in motor function of chronic stroke patients with severe paresis [36]. The present study revealed the feasibility of MI based BCI control of CPM training in chronic stroke patients with impairment of wrist extension. Among the participants who completed all the trainings, 80% showed apparent improvement of active wrist extension. Some patients could even extend 1 or 2 fingers while the flexor
ro of
muscles in the forearm were relaxed. Although this motor recovery was marginal and mostly non-functional, it still greatly encouraged these patients. The process of recovery from stroke-induced hemiplegia usually follows a relatively predictable,
-p
stereotyped series of events. Most recovery takes place within the first 3 months and
re
only minor additional recovery occurs beyond 6 months post-onset [23]. According to Twitchell et al.’s research, if stroke patients show no measurable grasp strength by 4
lP
weeks and prolonged flaccidity period, they may have poor prognosis of the hand [32].
na
Therefore, for our patients who showed little recovery of wrist and fingers after the initial post-onset stage, the outcomes of wrist extension recovery were meaningful.
ur
Even though, the improvement of modified BI was not obvious. There might be explanations. Firstly, the patients in our study were all chronic stroke survivors and
Jo
had underwent adequate conventional rehabilitation therapy for months or even years. In the acute stage, they might depend on the caregivers. But the goal of rehabilitation was to improve their independence and help them return to the society [26]. Most of the chronic stroke survivors could take good care of themselves regardless of the severity of motor impairment. Secondly, the present training aimed at improving the 14
motor function of affected wrists. Isolated improvement of this single movement might be too subtle to substantially influence the whole functional activity. An integration of several rehabilitation therapies might still be needed to increase the modified BI score. According to both literature and our previous studies of healthy volunteers, imagery of hand movement would induce ERD from contralateral hemisphere and
ro of
ERS from ipsilateral hemisphere [28,38-40]. This phenomenon mostly occurs in the centro-parietal region, and especially evident in channels C3 and C4. However, EEG activities usually vary among stroke patients. The location and severity of the brain
-p
lesion would also influence the extent of reorganization of the neural motor network
re
[41]. Therefore, the patients’ brain activation pattern during MI would be different from healthy volunteers. In the present study, the PSD features involving α (8-13Hz)
lP
and β (14-30Hz) rhythms were analyzed. It was found that typical ERD/ERS patterns
na
appeared in most patients as healthy volunteers after practice. However, two patients’ EEG patterns showed most evident ERD/ERS patterns around channels of P3 and P4,
ur
instead of C3 and C4. It might be due to a reorganized neural motor network in a lesioned brain.
Jo
The reason why only chronic stroke patients were included in the present study
was to minimize the potential influence of spontaneous recovery on the final outcomes. As the prognosis tended to be poor in chronic stroke patients with severe motor dysfunction, the potential of spontaneous recovery was minimal in our patients. Given that no further improvement was expected following long-term traditional 15
rehabilitation therapy in these patients, we considered it still be feasible to compare pre- and post-treatment evaluations. Even though, we believe that acute and subacute stroke patients could also benefit from this treatment. Further researches are still needed to verify this hypothesis. Conclusions In the present study, motor imagery based brain-computer interface in the control
ro of
of wrist continuous passive motion was applied in chronic stroke patients. The results
indicated that patients’ active wrist extension function improved after 6 weeks’ training. Meanwhile, the spatial and spectrum pattern of EEG signal also improved
-p
subsequent to the training. The study indicated that motor imagery based
re
brain-computer interface control of continuous passive motion might be a potentially effective therapy for chronic stroke patients.
lP
Limitations
na
There are still limitations in this study. Firstly, it was only a preliminary study in a series of chronic stroke patients. Although pre- and post-treatment comparison was
ur
acceptable in these patients, (an) adequate control group(s) might still be needed to acquire better evidences. Secondly, improvement in the wrist extension was still
Jo
insufficient for functional uses in daily life. This treatment may not substitute the whole traditional rehabilitation therapy. Moreover, studies including motor evoked potential (MEP) and DTI may further our underlying of brain reorganization mechanisms underlying this treatment.
16
Author Contributions Section Y.W. and J.X. designed the study and monitored the progress, data collection, and analysis. M.Z. and X.H. interpreted the data and made critical revision of the paper. R.L., T.G., G.L., and S.H. designed the study and collected and analyzed the data. J.L. collected the EEG data.
Acknowledgements This work was supported by Shanghai Sailing Program [grant number
ro of
16YF1415300]; Medical Research Project in Jing’an District [grant number 2015QN06]; Shanghai Rising-Star Program [grant number 19QA1409000]; Shanghai
Municipal Commission of Health and Family Planning [grant number 2018YQ02];
-p
Shanghai Commission of Science and Technology [grant number 18441903800];
re
Shanghai "Rising Stars of Medical Talent" Youth Development Program [grant
Jo
ur
na
2018YFC2001600].
lP
number RY411.19.01.10]; National Key R&D Program of China [grant number
17
References: [1] P. Langhorne, F. Coupar, A. Pollock. Motor recovery after stroke: a systematic review. Lancet Neurol 8 (2009) 741-754. [2] V.W. Mark, E. Taub, D.M. Morris. Neuroplasticity and constraint-induced movement therapy. Eura Medicophys 42 (2006) 269-284. [3] E. Taub, G. Uswatte, D.K. King, D. Morris, J.E. Crago, A. Chatterjee. A placebo-controlled trial of constraint-induced movement therapy for upper extremity after stroke. Stroke 37 (2006) 1045-1049. [4] K.C. Lin, H.Y. Chung, C.Y. Wu, H.L. Liu, Y.W. Hsieh, I.H. Chen, C.L. Chen, L.L. Chuang, J.S. Liu, Y.Y. Wai. Constraint-induced therapy versus control intervention in patients with stroke: a functional magnetic resonance imaging study. Am J Phys Med Rehabil 89 (2010) 177-185.
ro of
[5] P. Langhorne, J. Bernhardt, G. Kwakkel. Stroke rehabilitation. Lancet 377 (2011) 1693-1702.
[6] J. Decety. Do imagined and executed actions share the same neural substrate? Brain Res Cogn Brain Res 3 (1996) 87-93.
[7] S. Braun, M. Kleynen, J. Schols, T. Schack, A. Beurskens, D. Wade. Using mental practice in stroke rehabilitation: a framework. Clin Rehabil 22 (2008) 579.
-p
[8] M. Ietswaart, M. Johnston, H.C. Dijkerman, S. Joice, C.L. Scott, R.S. MacWalter, S.J. Hamilton.
Mental practice with motor imagery in stroke recovery: randomized controlled trial of efficacy. Brain 134 (2011) 1373-1386.
re
[9] J. Harris, A. Hebert. Utilization of motor imagery in upper limb rehabilitation: A systematic scoping review. Clin Rehabil 29 (2015) 1625481624.
[10] C.D. Garcia, C.J. Aboitiz. Effectiveness of motor imagery or mental practice in functional
lP
recovery after stroke: a systematic review. Neurologia 31 (2016) 43-52. [11] K.K. Ang, K.S. Chua, K.S. Phua, C. Wang, Z.Y. Chin, C.W. Kuah, W. Low, C. Guan. A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke. Clin Eeg Neurosci 46 (2015) 310-320.
na
[12] N. Mrachacz-Kersting, N. Jiang, A.J. Stevenson, I.K. Niazi, V. Kostic, A. Pavlovic, S. Radovanovic, M. Djuric-Jovicic, F. Agosta, K. Dremstrup, D. Farina. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J Neurophysiol 115 (2016) 1410-1421.
ur
[13] L.E.H.V. Dokkum, T. Ward, I. Laffont. Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke. Ann Phys Rehabil Med 58 (2015) 3-8.
Jo
[14] D.T. Bundy, L. Souders, K. Baranyai, L. Leonard, G. Schalk, R. Coker, D.W. Moran, T. Huskey, E.C. Leuthardt. Contralesional Brain–Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors. Stroke 48 (2017) 1908-1915.
[15] S. Blain-Moraes, R. Schaff, K.L. Gruis, J.E. Huggins, P.A. Wren. Barriers to and mediators of brain-computer interface user acceptance: focus group findings. Ergonomics 55 (2012) 516-525.
[16] J.L. Collinger, M.L. Boninger, T.M. Bruns, K. Curley, W. Wang, D.J. Weber. Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. J Rehabil Res Dev 50 (2013) 145-160. [17] Prasad G, Herman P, Coyle D, McDonough S, Crosbie J. Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. 18
J Neuroeng Rehabil. 2010;7:60. [18] 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. [19] Pichiorri F, Morone G, Petti M, Toppi J, Pisotta I, Molinari M, Paolucci S, Inghilleri M, Astolfi L, Cincotti F, Mattia D. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015;77(5):851-65. [20] Vourvopoulos A, Jorge C, Abreu R, Figueiredo P, Fernandes JC, Bermúdez I Badia S. Efficacy and Brain Imaging Correl ates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report. Front Hum Neurosci. 2019;13:244. [21] Saposnik G, Cohen LG, Mamdani M, Pooyania S, Ploughman M, Cheung D, Shaw J, Hall J, Nord P, Dukelow S, Nilanont Y, De Los Rios F, Olmos L, Levin M, Teasell R, Cohen A, Thorpe K, Laupacis A, Bayley M; Stroke Outcomes Research Canada. Efficacy and safety of non-immersive virtual reality exercising in stroke rehabilitation (EVREST): a randomised, multicentre,
ro of
single-blind, controlled trial. Lancet Neurol. 2016;15(10):1019-1027.
[22] C.M. McCrimmon, C.E. King, P.T. Wang, S.C. Cramer, Z. Nenadic, A.H. Do. Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J Neuroeng Rehabil 12 (2015) 57.
[23] E. Chung, J.H. Kim, D.S. Park, B.H. Lee. Effects of brain-computer interface-based functional
-p
electrical stimulation on brain activation in stroke patients: a pilot randomized controlled trial. J Phys Ther Sci 27 (2015) 559-562.
[24] Y. Jang, T. Kim, B. Lee. Effects of brain-computer interface-controlled functional electrical Occup Ther Int 23 (2016) 175-185.
re
stimulation training on shoulder subluxation for patients with stroke: a randomized controlled trial. [25] S.J. Cuccurullo.: Physical Medicine and Rehabilitation Board Review (3rd Ed), Demos Medical
lP
Publishing, New York, 2014.
[26] J. McCabe, M. Monkiewicz, J. Holcomb, S. Pundik, J.J. Daly. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity 981-990.
na
dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil 96 (2015) [27] G. Pfurtscheller, C. Neuper. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239 (1997) 65-68.
ur
[28] H. Ramoser, J. Müller-Gerking, G. Pfurtscheller. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8 (2000) 441. [29] G.R. Müller-Putz, R. Scherer, G. Pfurtscheller, R. Rupp. EEG-based neuroprosthesis control: A
Jo
step towards clinical practice. Neurosci Lett 382 (2005) 169-174. [30] A. Kbler, F. Nijboer, J. Mellinger, T. Vaughan, H. Pawelzik, G. Schalk, D. McFarland, N. Birbaumer, J. Wolpaw. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64 (2005) 1775-1777.
[31] C. Enzinger, S. Ropele, F. Fazekas, M. Loitfelder, F. Gorani, T. Seifert, G. Reiter, C. Neuper, G. Pfurtscheller, G. Muller-Putz. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Exp Brain Res 190 (2008) 215-223. [32] E. Buch, C. Weber, L.G. Cohen, C. Braun, N. Birbaumer. Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39 (2008) 910. [33] J.R. Wolpaw, N. Birbaumer, W.J. Heetderks, D.J. McFarland, P.H. Peckham, G. Schalk, E. 19
Donchin, L.A. Quatrano, C.J. Robinson, T.M. Vaughan. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8 (2000) 164. [34] Aman JE, Elangovan N, Yeh IL, Konczak J. The effectiveness of proprioceptive training for improving motor function: a systematic review. Front Hum Neurosci. 2015;8:1075. [35] Edwards LL, King EM, Buetefisch CM, Borich MR. Putting the "Sensory" Into Sensorimotor Control: The Role of Sensorimotor Integration in Goal-Directed Hand Movements After Stroke. Front Integr Neurosci. 2019;13:16. [36] Ramos-Murguialday A, Curado MR, Broetz D, Yilmaz Ö, Brasil FL, Liberati G, Garcia-Cossio E, Cho W, Caria A, Cohen LG, Birbaumer N. Brain-machine interface in chronic stroke: randomized trial long-term follow-up. Neurorehabil Neural Repair. 2019;33(3):188-198. [37] T.E. TWITCHELL. The restoration of motor function following hemiplegia in man. Brain 74 (1951) 443-480. [38] J. Li, H. Ji, L. Cao, D. Zang, R. Gu, B. Xia, Q. Wu. Evaluation and application of a hybrid brain
ro of
computer interface for real wheelchair parallel control with multi-degree of freedom. Int J Neural Syst 24 (2014) 1450014.
[39] H. Ji, J. Li, R. Lu, R. Gu, L. Cao, X. Gong. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme. Comput Intell Neurosci 2016 (2016) 1732836.
-p
[40] J. Li, L. Zhang, D. Tao, H. Sun, Q. Zhao. A prior neurophysiologic knowledge free tensor-based scheme for single trial EEG classification. IEEE Trans Neural Syst Rehabil Eng 17 (2009) 107-115.
re
[41] C.H. Park, W.H. Chang, S.H. Ohn, S.T. Kim, O.Y. Bang, A. Pascual-Leone, Y.H. Kim. Longitudinal changes of resting-state functional connectivity during motor recovery after stroke.
Jo
ur
na
lP
Stroke 42 (2011) 1357-1362.
20
Figure Legends Figure 1. Diagram of study design. (A) Flow chart of the study protocol. Patients who meet the inclusion criteria were recruited for 2-day practice of wrist extension motor imagery (MI) and then underwent four sessions of electroencephalographic (EEG) screening. Those who showed consistent MI related EEG pattern received baseline clinical assessments and completed a 6-week’s brain-computer interface (BCI)-driven continuous passive motion (CPM) training, followed by the
ro of
final clinical assessment. (B) The process of BCI-driven CPM training. The patient was wearing an
EEG cap of 24-channel high-resolution EEG system. His affected forearm and hand were attached to a
Jo
ur
na
lP
re
performing the visually cued MI tasks of wrist extension.
-p
CPM machine with straps. The CPM was controlled by the EEG signal while the patient was
21
Figure 2. Exemplar spatial and spectrum pattern of electroencephalographic (EEG). The figures showed the EEG mapping of an exemplar patient (Patient #6) during the 20-session brain-computer interface (BCI)-driven continuous passive motion (CPM) training. The patient tended to show a typical event-related desynchronization/event-related synchronization (ERD/ERS) pattern in the 8-30 Hz band power. When he imagined the movement of right (affected) wrist, significant increase in power (i.e., ERD) was recorded from the left (lesioned) hemisphere, while decrease in power (i.e., ERS) from the
Jo
ur
na
lP
re
-p
ro of
right. (L: left; R: right)
22
Figure 3. The time-frequency plots during the motor imagery (MI) task of affected wrist movement. A visualization example of the assembled tensor during the MI task of the right (affected) wrist extension. The spectrograms were shown at each channel according to channels distribution over the scalp, with time ranging from 0 to 4 s and frequency ranging from 8 to 30 Hz. The spectrograms at C3, C4, P3 and P4 channels were amplified at the bottom row. (ERD: event-related desynchronization;
Jo
ur
na
lP
re
-p
ro of
ERS: event-related synchronization)
23
f
No.
Sex
Age (yr)
Diagnosis
Affected UE
Post-stroke duration (mo)
Whether included
1
M
33
Ischemic stroke
R
6
Yes
2
F
58
Hemorrhagic stroke
L
6
3
M
54
Ischemic stroke
L
6
4
F
61
Ischemic stroke
R
12
5
M
64
Ischemic stroke
L
20
6
M
42
Hemorrhagic stroke
R
7
M
32
Ischemic stroke
R
8
M
75
Ischemic stroke
9
M
43
10
M
11
No
NA
pr No
Pr
e-
Yes
Yes Yes
6
No
L
17
Yes
Hemorrhagic stroke
R
13
Yes
32
Hemorrhagic stroke
R
7
Yes
M
56
Ischemic stroke
L
10
Yes
12
M
32
Hemorrhagic stroke
R
9
Yes
13
M
37
Hemorrhagic stroke
L
12
Yes
14
M
63
Ischemic stroke
R
8
Yes
na l
Baseline ROM of affected wrist
10° (10° fle ~ 0° ext) 0° (0° fle ~ 0° ext) NA
31
Jo ur
oo
Table 1. Patient Characteristics
0° (0° fle ~ 0° ext) 10° (10° fle ~ 0° ext) NA 0 ° (0 ° fle ~ 0 ° ext) 0 ° (0 ° fle ~ 0 ° ext) 10° (10° fle ~ 0° ext) 0 ° (0 ° fle ~ 0 ° ext) 10° (10° fle ~ 0° ext) 10° (10° fle ~ 0° ext) 0 ° (0 ° fle ~ 0 °
Completion ROM of affected wrist
Baseline BI
Comple tion BI
45° (15° fle ~ 30° ext)
90
90
0°(0° fle ~ 0° ext)
70
75
NA
NA
NA
NA
NA
NA
0°(0° fle ~ 0° ext)
90
90
35° (10° fle ~ 25° ext)
100
100
NA
NA
NA
15° (10° fle ~ 5° ext)
70
70
0°(0° fle ~ 0° ext)
70
80
45° (15° fle ~ 30° ext)
100
100
20° (5° fle ~ 15° ext)
60
65
45° (10° fle ~ 35° ext)
100
100
40° (10° fle ~ 30° ext)
100
100
25° (5° fle ~ 20° ext)
90
95
24
M
32
Hemorrhagic stroke
R
11
Yes
16
F
64
Ischemic stroke
L
11
Yes
17
M
63
Ischemic stroke
R
11
18
M
37
Hemorrhagic stroke
L
17
19
M
44
Hemorrhagic stroke
R
20
M
63
Ischemic stroke
R
21
M
43
Ischemic stroke
22
M
77
23
M
24
f
10° (10° fle ~ 0° ext)
0 ° (0 ° fle ~ 0 °
pr
15
oo
ext)
Pr
e-
Yes Yes
ext)
10° (10° fle ~ 0° ext)
10° (10° fle ~ 0° ext) 0 ° (0 ° fle ~ 0 °
50° (15° fle ~ 35° ext)
100
100
5°(5° fle ~ 0° ext)
50
60
40° (10° fle ~ 30° ext)
90
90
45° (10° fle ~ 35° ext)
100
100
20° (5° fle ~ 15° ext)
70
80
50° (10° fle ~ 40° ext)
90
90
15° (5° fle ~ 10° ext)
95
100
Yes
12
Yes
L
6
Yes
Ischemic stroke
L
8
No
NA
NA
NA
NA
56
Ischemic stroke
L
6
No
NA
NA
NA
NA
M
50
Hemorrhagic stroke
L
43
Yes
40° (10° fle ~ 30° ext)
60
70
25
M
17
Hemorrhagic stroke
R
19
Yes
25° (5° fle ~ 20° ext)
100
100
26
M
47
Hemorrhagic stroke
R
23
Yes
55° (15° fle ~ 40° ext)
55
60
Jo ur
na l
6
ext) 10° (10° fle ~ 0° ext) 5 ° (5 ° fle ~ 0 ° ext)
10° (10° fle ~ 0° ext) 5 ° (5 ° fle ~ 0 ° ext) 10° (10° fle ~ 0° ext)
M: male; L: left; R: right; UE: upper extremity; yr: year; mo: month; ROM: range of motion; BI: Barthel index; fle: flexion; ext: extension; NA: not applicable
25