Effect of Post-Stroke Motor Training on EEG Movement-Related Cortical Potentials

Rehabilitation of post-stroke patients with motor impairments promotes re-learning of lost motor functions through the brain neuroplasticity. Monitoring of electroencephalogram (EEG) signals has the potential to show neuroplasticity changes that take place during motor training. In this study, an EEG-derived time-domain pattern namely movement-related cortical potential (MRCP) was deployed to assess the effect of motor training in seven post-stroke patients. Patients were divided into two groups; group A comprising four subjects with supratentorial lesions and group B consisting of three subjects with infratentorial lesions. Both groups participated in motor training with an AMADEO hand rehabilitation device. During pre and post-training periods, EEG signals at eight selected electrodes were recorded. In addition, hand-kinematic parameters, and clinical tests were measured at the beginning and the end of all training sessions.


Introduction
According to the recent annual report of the World Stroke Organization, approximately 14 million people had their rst-time stroke in 2019 and 80 million people lived with the impact of stroke globally (1). The effects of stroke vary among patients and it depends on the type of stroke, the brain part that is damaged, and the amount of damage caused by it. Generally, stroke results in changes in the level of consciousness, changes in behavioral styles, and impairment of cognition, perception, language, sensory, and motor skills. Motor skills impairment is the predominant effect of the stroke. Speci cally, the impairment of hand functions limits the independence of stroke survivors.
After the stroke, re-learning of lost motor functions is achieved with training strategies such as physiotherapy (2,3), constraint-induced movement therapy (4,5), mirror-box therapy (6,7), virtual reality therapy (8,9), and robot-assisted therapy (10,11). All these types of training strategies promote the mechanism of neuroplasticity. The neuroplasticity is a neurological adaption in the brain where new neural pathways are established, existing pathways are reinforced and adjacent surviving neuronal tissues assume the role of the damaged neuronal tissues (12,13).
The effect of rehabilitation training on brain activities helps to better understand the mechanism of recovery after stroke which in turn can facilitate the development of advanced rehabilitation training strategies. Many technologies are used in the literature to determine the effect of various motor training.
Among these technologies, EEG is a low-cost, safe, and user-friendly method of recording brain activity that has been a popular choice in the literature to determine training effects on brain activities during motor tasks.
There are two types of EEG-derived patterns that are associated with movement and have been used to assess the effect of motor training. One of these patterns is known as event-related desynchronization/event-related synchronization (ERD/ERS). ERD is a frequency-speci c power decrease mostly in alpha (8)(9)(10)(11)(12)(13) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency bands of EEG during the preparation of a movement until its onset which then increases after motor execution denoted by ERS (33). The other EEGderived pattern appears in a time-domain called movement-related cortical potential (MRCP). MRCP is a slow event-related potential that appears in the delta frequency band of EEG as a direct-current shifts up to 2 s before cue-based as well as self-initiated movements (34). MRCP has three pre-movement components, which have been widely addressed in the literature that could indicate the effect of motor training (18)(19)(20)(21)(22)(23)(24). The rst pre-movement component is a slow decrease in the cortical potential that starts around 2 s before movement onset (in this paper it is called Bereitschaftspotential 1 (BP1)). The second pre-movement component is a steeper decrease in cortical potential and starts at about 0.5 s before movement onset (termed as Bereitschaftspotential 2 (BP2)). The third pre-movement component of the MRCP is the lowest negative potential near the movement onset (de ned as the negative peak (Npeak)).
In the literature, the variations in the amplitude of MRCP components due to motor training or skill acquisition have been reported with con icting results. For instance, Taylor (18) observed that the amplitude of MRCP increased with the improvement in response time after single-session training of nger motor tasks. Lang et al. (19) demonstrated an increase in the MRCP amplitude with improvement in task performance during a visual-motor activity. Niemann et al. (20) observed a signi cant decrease in the amplitude at some electrodes during a complex hand movement task performed by healthy participants. Some other studies also reported a decrease in the amplitude of the MRCP after the subjects achieved competency in motor task with practice (21)(22)(23)(24). Notably, all these studies demonstrate the effects of various motor training protocols in healthy participants on the MRCP signal. Moreover, these studies overlook the factor of stroke lesion location during motor training design and analysis of the results, although several authors reported that the post-stroke recovery during motor skills acquisition after rehabilitation training depends upon the lesion location (35)(36)(37)(38)(39).
This study focuses on the motor training effect occurring in post-stroke patients using the MRCP signal. It also aims to investigate the effect of different lesion locations on the MRCP signal after the stroke patients completed the rehabilitation training. By establishing the effect of motor training on MRCP features with post-stroke patients, this study can be a stepping-stone in guiding therapists to adjust the di culty of rehabilitation training to continually challenge the subjects, induce higher degrees of brain neuroplasticity, and to enhance consequent therapeutic outcomes.
In the approach deployed in this research, post-stroke patients underwent robot-assisted motor training of their affected hand with the help of an AMADEO rehabilitation device. An EEG acquisition system extracted MRCP signals during pre and post-training protocols. The improvements in hand motor skills after the training were determined using clinical tests and hand-kinematic parameters measurement. The clinical tests included the Fugl-Meyer Assessment (FMA) for upper extremity and Motor Assessment Scale (MAS). The hand-kinematic parameters consisted of hand strength measured during exion (forceexion), hand strength measured during extension (force-extension), and hand range of movement (HROM). Also, variations in MRCP features were correlated with improvements in hand motor skills in this paper.
Compared to the previous work, the study presented in this paper is novel and unique. The MRCP signal has been deployed previously but only for healthy subjects in non-clinical applications. For example, Wright et al. (23) observed a reduction in the amplitude of MRCP features when participants learned to play guitar after ve weeks of training. Jochumsen et al. (24) reported reduced MRCP features' amplitude when healthy participants completed 6 training sessions of simulated laparoscopic surgery training with their non-dominant hand. In this study, the MRCP signal is used to demonstrate the effect of designed robot-assisted training in post-stroke patients and identifying the effect of stroke lesion location on the rehabilitation process. The results produced by the study are validated by benchmarking against standard clinical methods and procedures. This paper is structured as follows. The material and method section provides details of equipment used in the experimental work, participants' information, pre and post-training protocols, motor training protocols as well as data processing details and statistical analysis. The analysis of the EEG data, the clinical test results, the hand-kinematic parameters' results for both groups A and B, and extended study of group B are provided in the following section. The discussion section highlights the main ndings, implications, and limitations of the study. At the end of the paper, some conclusions are drawn and future potential of the work is discussed.

AMADEO Hand Rehabilitation Device
Robot-assisted therapy is widely investigated and coming into clinical practice for the rehabilitation of post-stroke patients (40,41). AMADEO (Tyromotion GmbH, Graz, Austria) is a state-of-the-art rehabilitation device designed for ne motor skill improvement in patients with spinal cord injury and stroke (42). It is gaining signi cant interest in both research and clinical communities (41). AMADEO is specially designed for distal upper-extremity motor recovery of patients (43). It has ve degrees of freedom that allow passive, assistive as well as the active movement (with the help of 2D interactive games) of ngers and thumb. Many studies have used AMADEO for post-stroke rehabilitation. For instance, Xianwei et al. (44,45) used AMADEO for ne nger motor recovery of post-stroke patients. A novel algorithm incorporating assist-as-needed, integrated into AMADEO demonstrated a 35% increase in the hand movement after multi-session training. The same research group studied the effect of 18 sessions of motor training with AMADEO on stroke patients and showed signi cant improvements in nger strength, range of hand movement, and coordination (46).
In this study, an AMADEO standard therapy program is used for motor training of patients' stroke affected hands. The force-exion, force-extension, and HROM parameters (termed as hand-kinematic parameters) of all patients during pre and post-training protocols were measured using the AMADEO assessment tool.

EEG Acquisition System
EEG signals were acquired during pre and post-training protocols to extract the MRCP signal for selfpaced hand movements. The EEG signal was recorded using 32-channel Ag/AgCl Quick-Cap (Compumedics-Neuroscan) according to the 10-20 electrode positioning system. The Grael 4K EEG ampli er was con gured for a sampling frequency of 2048 Hz, bandwidth DC-2048 Hz, resolution 24-bit, and input range of 600 mVpp. The FPz electrode was used as a ground electrode and a separate electrode was placed on the ipsilateral earlobe as a reference. The impedance of each electrode was set below 5 kΩ. The EEG acquisition software used in this study is CURRY 8X (Compumedics-Neuroscan), which allows both o ine and online data processing.

Participants
The following inclusion criteria were designed for recruiting participants:  Table 1. Every patient also received standard care at a local hospital, in addition to our intervention protocol. The participants gave their written informed consent before the experiment commencement. The MAS-hand movement test scores in Table 1, acquired at the beginning of the motor training program, indicate that the patients in group A had better baseline nger movements while group B patients had limited nger movements consistent with the location of the lesion in their brain stem.

Motor Training Protocol
AMADEO standard therapy programs were used for motor training of the affected hand for both groups A and B. AMADEO allows four basic training programs which include Continuous Passive Motion (CPM), CPMplus, Assistive therapy, and Active therapy programs. In the beginning, the HROM for each patient was set according to the AMADEO protocol to the maximum potential range depending on each patient's hand size. The duration of each training session was 30 minutes and patients received three training sessions weekly up to four weeks (12 training sessions). The total training duration for each patient was 360 minutes in one month. However, patient SP7 completed 10 motor training sessions instead of 12 due to personal circumstances. The speci c training programs for groups A and B are presented in Table 2. 2) CPMplus training mode for 5 minutes; 3) Assistive training mode for 10 minutes; and 4) Active training mode (2D interactive games) for 10 minutes.
Group B 1) CPM training mode for 10 minutes; 2) CPMplus training mode for 10 minutes; and 3) Assistive training mode for 10 minutes.
Although the total duration of motor training for group A was designed to be the same as for group B, active training mode was included only in group A training protocol because stroke patients in group B were unable to play the 2D games with their initial nger movements. At rst, it was decided to compare the results of four weeks of training for both groups. However, it was anticipated that group B participants might require longer training-period due to lesion location in their brain stem (48).

Pre and Post-Training Protocols
Three baseline measurements were recorded for each patient in both groups A and B: (1) The EEG signal was acquired while the subjects were asked to perform self-paced simple hand grasping movements with their affected hand in 8 to 10 blocks of 10 trials each. The time gap between any two trials was randomly varied from 8 s to 10 s. Patients focused their vision on a cross-mark to avoid random eye-movement artifacts. On each movement trial, a digital trigger was manually sent to the acquisition software (CURRY 8X, Compumedics-Neuroscan) to divide the continuous EEG data recording into epochs of 10 s duration.
(2) The clinical tests namely FMA test (wrist and hand sections only) (49) as well as the MAS tests (50), for both hand movement and advanced hand movements, were applied to assess the current hand motor abilities of patients. These clinical tests were denoted as FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements in this paper.
(3) The force-exion, force-extension, and HROM parameters for the affected hand were measured using the assessment tool on the AMADEO hand rehabilitation device.
These three measurements were repeated on day 13 after completion of 12 training sessions.

Data Processing and Statistical Analysis
Eight single EEG electrodes were used for the analysis (FC3, FC4, C3, C4, CP3, CP4, Cz, and CPz). In the literature, the C3, Cz, and C4 electrodes are commonly used to extract MRCP signals for hand motor tasks (18)(19)(20)(21)(22)(23)(24). In addition, ve other electrodes (FC3, FC4, CP3, CP4, and CPz) were also explored in this experiment. The positions of all these selected electrodes in 32-channels Quick-Cap are shown in red color in Fig. 1. EEG signals from each selected electrode were rst passed through a notch lter (49)(50)(51) to remove any power line noise. They were then passed through a low-pass lter with 5 Hz cut-off frequency and a high-pass lter with a 0.5 Hz cut-off because MRCP signals lie in the 0.5-5 Hz delta band range (51). The ltered EEG data were then divided into epochs using event triggers. The duration of these epochs was set from − 5 s to 5 s and where 0 s was the onset of the movement. MRCP has the lowest potential around the movement onset point (24,34). Along with EEG data analysis, clinical tests, hand force, and HROM measurements were also analyzed.
The clinical tests (FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements) were performed three times by each patient and the best scores were recorded according to the general rule of administration for these clinical tests. Whereas force-exion, force-extension, and HROM parameters were also measured three times but their average values were used during analysis of the results.
Statistical signi cance was calculated in all three measurements (MRCP signal features, clinical tests, as well as hand-kinematic parameters) using a two-tailed paired t-test. The signi cant level of the t-test is reported at the alpha value of p < 0.05.

EEG Data Analysis Results
In this section, results obtained from EEG data analysis for groups A and B are presented. For both groups, visible MRCP signals were obtained using the patients' data at all eight selected electrodes during pre and post-training periods. The averaged pre and post-training MRCP signals at all selected electrodes (ILFC, ILC, ILCP, CLFC, CLC, CLCP, Cz, and CPz) for group A and group B are shown in Fig. 2

Clinical Tests Results
FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements' tests were executed on day 0 and day 13 of the designed robot-assisted training for each stroke patient in group A and group B. These clinical tests were used to determine the physical improvements in the hand motor abilities of the patients.  Table 3. For group A, FMA-wrist (p = 0.006), FMA-hand (p = 0.043) as well as MAS-hand movements (p = 0.035). However, the MAS-advanced hand movement clinical test did not show statistically signi cant improvement (p = 0.252).

Results for Hand-Kinematic Parameters
The AMADEO assessment tool allows the measurement of force-exion, force-extension, and average HROM of ngers and thumb. To nd the changes in these hand-kinematic parameters after the training, force-exion, force-extension, and HROM were calculated at the pre and post-training periods for group A and group B.
For group A, Table 5 shows the mean (± SD) values of force-exion, force-extension, and HROM obtained during pre and post-training protocols. The statistical signi cance levels between pre and post-values of all three kinematic parameters were calculated using the paired t-test. The pre and post-values of all these kinematic parameters for hand movement recovery (force-exion, p = 0.028; force-extension, p = 0.048; HROM; p = 0.039) showed statistically signi cant improvements.

Extended Training of Group B and its Results
Apart from the FMA-hand score, the above results revealed that 4 weeks of motor training did not have a signi cant effect on MRCP Npeak amplitude or other clinical tests and hand-kinematic parameters' results for post-stroke patients in group B. Therefore, it was decided to extend the training period for all participants in group B for another 4 weeks to determine whether the extension of the hand motor training affects MRCP Npeak feature, clinical tests, and hand-kinematics parameters.
The three brain stem stroke patients in group B underwent another phase of motor training that consisted of 4 weeks (12 sessions, 3 sessions per week) of advanced training protocols using the AMADEO device.
During this extended training, patients received four levels of training each day consisting of CPM training mode for 5 minutes, CPMplus training mode for 5 minutes, Assistive training mode for 10 minutes, and Active training mode for 10 minutes. In this way, group B participants received two-phases of training using the AMADEO robot in which the second phase of training was slightly more intense compared to the rst phase as it included training on active therapy. Moreover, the same three assessment procedures were conducted at the end of 8 weeks of the designed robot-assisted training of hand as performed during the beginning of training (week 0) and at the end of the rst phase of training (week 4).
The results obtained from the data analysis of week 8 were compared to that obtained during week 0 and week 4 to measure the effect of extending the training on MRCP Npeak amplitude and physical improvement in hand motor skills. Figure 5 shows the averaged MRCP signal plots at all eight electrodes, extracted from EEG data acquired before the rehabilitation training (week 0), at the end of the rst phase of training (week 4) and after the completion of both phases of training (week 8) for brain stem stroke patients of group B. Visual inspection of the plots reveal that averaged Npeak amplitude of MRCP signal was decreased at week 8 with respect to corresponding value at week 0 for all electrode positions.
Whereas, as stated above, the Npeak amplitude at week 4 was increased at ipsilateral electrodes, slightly decreased at contralateral and CPz electrodes, and remained the same at the Cz electrode compared to week 0.
To assess the signi cance of these variations, the MRCP Npeak feature was analyzed. The Npeak feature of the MRCP signal was extracted from the acquired EEG data after completion of two-phase training of group B. Figure 6 shows the bar-chart representation of average Npeak amplitudes at all eight electrodes for group B. A consistent decrease in average Npeak amplitude was observed for all selected electrodes after a total of 8 weeks of training when it is compared with week 0. When the paired t-test was applied, a signi cant change in Npeak was obtained at CLC (p = 0.01) and CPz (p = 0.04) electrodes. The signi cance level is indicated by a '*' symbol in Fig. 6. In contrast to these results, change in Npeak amplitude at all eight electrodes was not consistently decreased after the rst 4 weeks of motor training compared to week 0. These results of MRCP Npeak analysis suggest that 4 weeks of rehabilitation is not a su cient time to obtain consistent EEG signal changes for the brain stem stroke patients in group B. This outcome is consistent with clinical observations that patients with brain stem strokes are typically slower to recover motor function than patients with supratentorial strokes (48).

Discussion
The main purpose of this study was to investigate possible changes in the features of the MRCP signal when two groups of post-stroke patients with different lesion locations receive robot-assisted rehabilitation training for their impaired hand using AMADEO robot.
The EEG data analysis revealed that all participants in both groups A and B were able to generate MRCP signals during the self-paced motor task of their affected hand at all eight selected electrode positions.
The MRCP signal's Npeak was investigated for group A and group B separately to explore whether it is increased or decreased after the completion of 12 The reported results reveal that the Npeak amplitude of the MRCP signal is decreased consistently in patients with supratentorial strokes (group A) after four weeks of training while it is decreased consistently in patients with brain stem strokes (group B) after eight weeks of training. These Npeak changes of both groups also correlate with improvements in clinical tests and hand-kinematic parameters' results. These results suggest that 4 weeks of rehabilitation is not su cient time to induce signi cant MRCP signal changes for the brain stem stroke patients who comprise group B. This outcome is consistent with the clinical observation that patients with brain stem strokes are typically slower to recover motor function than patients with supratentorial strokes (48).
The decrease in MRCP Npeak amplitude after the designed robot-assisted motor training re ects that neurological pathways become more established so that fewer cortical resources are needed for motor planning and execution of tasks. This hypothesis is supported by studies in healthy participants available in the literature (20)(21)(22)(23)(24). However, further investigations are required to validate the occurrence of neuroplasticity.
To the best of our knowledge, this study is the rst attempt to use the MRCP signal as an assessment tool to determine the effect of motor training in actual stroke patients. EEG is an easy and cost-effective method to assess changes in brain activation during functional motor activities (24). The results of this study indicate that EEG has future potential in clinical utility for stroke rehabilitation.
A larger number of participants in the study would have strengthened our con dence in the results. However, the number of potential participants was limited by the clinical availability of suitable participants within the time frame of the study. Participants in the study were relatively heterogeneous with regard to the length of time from stroke to onset of the intervention (see Table 1). It may be the case that with a more homogeneous group of participants more uniform and statistically signi cant data could have been extracted. However, our inclusion criteria had to be wide; otherwise, clinical availability would have not allowed us to recruit a su cient number of participants.

Conclusion
This paper demonstrated the feasibility of using the EEG signal as an assessment parameter for the determination of motor training outcomes for stroke patients with different lesion locations. We found that there was a statistically signi cant decrease in the Npeak amplitude of the MRCP signals for stroke patients with supratentorial lesions and they also demonstrated associated signi cant improvements in hand-kinematic parameters and clinical test outcomes after four weeks of training. While the infratentorial (brain stem) stroke patient showed a statistically signi cant decrease in Npeak as well as a signi cant improvement in kinematic parameters and clinical tests after eight weeks of training. We conclude that MRCP could be used as an assessment tool to determine motor training effects in both supratentorial and infratentorial strokes. Moreover, this technology has real potential as a practical and inexpensive therapeutic tool that could be used by therapists to detect neuroplasticity responses during stroke rehabilitation, and allow them to adjust the intensity of training challenges accordingly to enhance neuroplasticity responses and therefore therapeutic outcomes. 2014/400), and all procedures performed under the approved study protocol. The written informed consent was obtained from all the participants before the experiment commencement.

Consent for publication
All the participants involved in the study provided their written informed consent for publication.

Availability of data and materials
The data collected during this study are available from the corresponding author on reasonable request Positions of selected electrodes for this experiment Average MRCP signals for group A at all channels over multiple-session training  Average MRCP signals for group B after extended training at all channels over multiple-session training Figure 6 Mean absolute Npeak amplitude at week 0, week 4 and week 8 for group B