Effect of Electroencephalography-based Motor Imagery Neurofeedback on Mu Suppression During Motor Attempt in Patients with Stroke

doi:10.21203/rs.3.rs-5106561/v1

Objective

The primary aims of this study were to explore the neurophysiological effects of motor imagery neurofeedback using electroencephalography (EEG), specifically focusing on mu suppression during serial motor attempts and assessing its potential benefits in patients with subacute stroke.

Methods

A total of 15 patients with hemiplegia following subacute ischemic stroke were prospectively enrolled in this randomized cross-over study. This study comprised two experiments: neurofeedback and sham. Each experiment included four blocks: three blocks of resting, grasp, resting, and intervention, followed by one block of resting and grasp. During the resting sessions, the participants fixated on a white cross on a black background for 2 minutes without moving their upper extremities. In the grasp sessions, the participants were instructed to grasp and release their paretic hand at a frequency of about 1 Hz for 3 minutes while fixating on the same white cross. During the intervention sessions, neurofeedback involved presenting a punching image with the affected upper limb corresponding to the mu suppression induced by imagined movement, while the sham involved mu suppression of other randomly selected participants 3 minutes. EEG data were recorded during the experiment, and data from C3/C4 and P3/P4 were used for analyses to compare the degree of mu suppression between the neurofeedback and sham conditions.

Results

Significant mu suppression was observed in the bilateral motor and parietal cortices during the neurofeedback intervention compared with the sham condition across serial sessions (p < 0.001). Following neurofeedback, the real grasping sessions showed progressive strengthening of mu suppression in the ipsilesional motor cortex and bilateral parietal cortices compared to those following sham (p < 0.05), an effect not observed in the contralesional motor cortex.

Conclusion

Motor imagery neurofeedback significantly enhances mu suppression in the ipsilesional motor and bilateral parietal cortices during motor attempts in patients with subacute stroke. These findings suggest that motor imagery neurofeedback could serve as a promising adjunctive therapy to enhance motor-related cortical activity and support motor rehabilitation in patients with stroke.

neurofeedback

electroencephalography

motor imagery

mu suppression

stroke

upper extremity

rehabilitation

Stroke is a leading cause of death and disability. Although stroke mortality rates have decreased, the burden of stroke remains high, considering the growing and aging population [1]. Therefore, comprehensive and tailored rehabilitation strategies are gaining increasing attention. For stroke rehabilitation, organized stroke unit, task-specific and repetitive training, goal setting and early supported discharge are recommended [2]. Recent technological advancements have spurred various efforts to enhance stroke rehabilitation.

Among these advances, neurofeedback using electroencephalography (EEG) or functional magnetic resonance imaging (MRI) has been explored in many studies. Neurofeedback is a biofeedback system in which neural activity is measured and presented in real-time to modulate the activity of targeted neural substrates [3]. A previous review demonstrated that neurofeedback could enhance motor recovery in patients with stroke when combined with conventional physical therapy, non-invasive brain stimulation, or robots [4]. As acquisition systems for neurofeedback, EEG with its high temporal resolution and functional MRI with its high spatial resolution are the two most representative methods. Notably, EEG has traditionally been used in many studies by leveraging the event-related desynchronization (ERD) of the mu rhythm or central beta rhythm [5].

Previous studies have reported somatotopic ERD patterns during motor imagination, which form the basis of EEG-based neurofeedback research [6]. Thereafter, paradigms for providing motor imagery neurofeedback through reliable ERD monitoring were proposed [7, 8]. Bartur et al. demonstrated that the magnitude of ERD in the motor cortex during motor attempt is directly proportional to the EMG activity of the paralyzed arm and inversely proportional to lesion size [9]. Additionally, a previous review highlighted that stroke recovery involves the reorganization of ERD during motor attempts, with changes observed in both the ipsilesional and contralesional hemispheres [10]. While previous research has explored the potential of motor imagery-based neurofeedback and established a link between ERD during motor attempts and stroke recovery, there is a paucity of studies examining how motor imagery-based neurofeedback specifically affects ERD during actual motor attempts. Moreover, considering that neurofeedback may serve as a potential adjunctive modality to enhance neuroplasticity during task-specific rehabilitation [11], further research is warranted to investigate how motor imagery-based neurofeedback influences ERD during real motion tasks. Understanding this relationship could provide valuable insights into the optimization of neurofeedback interventions to improve rehabilitation outcomes.

Therefore, our study aimed to investigate the impact of motor imagery-based neurofeedback on mu suppression in the sensorimotor cortex during a real motor attempt in patients with unilateral upper limb weakness after stroke. We anticipate that the findings will provide insights into how these changes during motor imagery neurofeedback could influence neuroplasticity during motor attempts and how they might be integrated into conventional task-based rehabilitation strategies.

Participants

Patients with hemiplegia after early subacute (from 1 week to 5 weeks) ischemic stroke were prospectively enrolled. Patients with cognitive impairment (Mini-Mental State Examination score < 16), delirium, depression, anxiety, or other uncontrolled medical diseases were excluded. Initially, 20 patients consented to participate and were enrolled in the study. However, three patients dropped out due to discomfort in maintaining posture and wearing the EEG, one patient was excluded due to an inability to follow instructions caused by decreased attention, and one patient was excluded due to poor EEG quality. Consequently, a total of 15 patients were included in the final analysis. This study was approved by the Institutional Review Board of Keimyung University Dongsan Hospital (IRB number 2021-04-112-003). All participants signed the written consent form.

Study design

This randomized cross-over study consisted of two experiments: neurofeedback and sham (Fig. 1). Each experiment consisted of four blocks (three blocks of resting, grasp, resting, and intervention and one block of resting and grasp). In the resting session, a white fixation cross with a black background appeared and was fixed for 2 minutes without upper extremity movement. In the grasp session, the same white fixation cross appeared, and the participants were instructed to grasp and release their weak hand at a frequency of about 1 Hz for 3 minutes. Subsequently, another 2-minute resting session followed. Then, the participants spent 3 minutes imagining movements in the neurofeedback session, during which a boxing image was generated based on mu suppression detected from the EEG. However, during the sham condition, an EEG recorded from another participant's neurofeedback session was used to trigger the appearance of the boxing image on the screen instead of the participant's real EEG. The first patient underwent neurofeedback first, with the sham condition using their own EEG. For subsequent patients, the sham EEG was randomly selected from the neurofeedback sessions of the previous patients. The two experiments were conducted in a randomized crossover design with a washout period of at least 3 days.

Neurofeedback mechanism

The neurofeedback system is based on the characteristics of the asymmetry of mu suppression in the motor cortex. The EEG data from the C3 and C4 channels were filtered using a bandpass filter (a 5th-order Butterworth) that passed signals between 8 and 12 Hz. The EEG signal was then segmented with an epoch size of 1 second, overlapping every 1/16 second to calculate the mu band power.

First, we used the mean and standard deviation of the mu power of the C3 and C4 brainwaves acquired for a 1-minute resting session to normalize the data such that the incoming mean mu power is 0 when it is at the mean value and + 1 or -1 at a point that is 2 standard deviations away from the mean. The output index can, therefore, move along a line that outputs + 1 when the incoming mu power is at the mean minus 2 standard deviations, 0 when it is at the mean, and − 1 when it is at the mean plus 2 standard deviations; thus, the index reflects higher values when the mu power is lower and lower values when it is higher.

After obtaining normalized levels for both the ipsilesional and contralesional sides, contrast scores were calculated for each motor imagery training session. For paretic upper limb motor imagery training, the contrast score was obtained by subtracting the contralesional index from the ipsilesional index. This score represented the relative strength of ipsilesional mu suppression compared with contralesional mu suppression. When the contrast score exceeded 0, it accumulated, and once the accumulated score reached 2, an arbitrarily determined threshold, a punch to the target in the boxing game was triggered.

EEG acquisition

EEG data were collected from 19 dry electrodes using DSI-24 (Wearable Sensing, San Diego, CA, USA). The electrodes were arranged based on the International 10–20 system, and the data were sampled at 300Hz frequency.

The recorded EEG was band pass filtered with a 4–30 Hz and rejected artifacts. Then, it was segmented with a 2-second epoch. The epochs with an average amplitude exceeding 150 uV were excluded. Baseline correction and the linear detrend were performed for each epoch. The mu band power was extracted using power spectrum density analysis, and the values of the mu band power were averaged. The mu band power for each grasp session was compared with that of resting session. The degree of mu suppression was calculated using the log ratio. In the EEG topography analysis, the lesion side was standardized to the left hemisphere. Consequently, the EEG data from patients with left-sided lesions were used without modification, whereas the EEG data from patients with right-sided lesions were horizontally flipped.

Statistical analysis

Mu suppression in the motor and parietal cortices was used for analysis. Repeated-measures analysis of variance (ANOVA) for intervention (neurofeedback vs sham) x time (from task 1 to task 3 or from grasp 1 to grasp 4) was used to identify significant factors associated with mu suppression. If there was a statistical significance for intervention or time, post-hoc analysis to compare the effect between neurofeedback and sham or between time points was conducted using Bonferroni-corrected p-values. Statistical significance was set at p < 0.05. All analyses were performed using R 4.4.0 (R Foundation for Statistical Computing, Vienna, Austria).

A total of 15 patients with subacute cerebral infarctions were recruited for this study. The baseline characteristics of the study participants are shown in Table 1. The mean age of the participants was 66.5 years with two female participants. During the neurofeedback sessions, the average number of punch per 3-minute session was 13.0 (95% confidence interval (CI) of 9.5–16.5) for task 1, 15.8 (95% CI of 12.3–19.3) for task 2, and 15.7 (95% CI of 13.2–18.1) for task 3. The average time required to generate a sufficient neurofeedback response per punch was 18.3 seconds (95% CI of 7.2–29.4) for task 1, 10.6 seconds (95% CI of 8.3–12.9) for task 2, and 10.0 seconds (95% CI of 8.3–11.8) for task 3. There was no statistical significance for trends in the average number of punch and the average time.

During the neurofeedback intervention, mu suppression was evident, whereas such suppression was not visible during the sham intervention (Fig. 2A). Figure 2B shows the line charts of mu suppression in the ipsilesional and contralesional motor and parietal cortex. In the ipsilesional motor cortex, ANOVA indicated that the type of intervention was a significant factor influencing mu suppression (p < 0.001). Mu suppression was significantly greater during tasks 1, 2, and 3 during the neurofeedback intervention (p = 0.002, 0.044, and 0.032, respectively) than during the sham intervention. In the contralesional motor cortex, ANOVA indicated that the intervention significantly influenced mu suppression (p < 0.001). Mu suppression was greater during tasks 1 and 3 in the neurofeedback (p = 0.039 and 0.014, respectively) than in the sham condition. For the ipsilesional parietal cortex, ANOVA showed that the intervention was a significant factor (p < 0.001), and mu suppression was greater during tasks 1 and 3 in the neurofeedback (p = 0.001 and 0.019, respectively). In the contralesional parietal cortex, ANOVA showed that the intervention was a significant factor (p < 0.001), and mu suppression was greater during tasks 1 and 3 (p < 0.001 and < 0.001, respectively).

In the real grasp attempt session of the neurofeedback experiment, mu suppression progressively strengthened as the sessions proceeded, whereas, in the grasp session of the sham experiment, no significant changes in mu suppression were observed. The EEG topography for each experiment is presented in Fig. 3A, and the line charts of mu suppression are presented in Fig. 3B. In the ipsilesional motor cortex, ANOVA indicated that the intervention was a significant factor influencing mu suppression (p = 0.003), and there was a significant interaction between the intervention and time (p = 0.026). In the post-hoc analysis, mu suppression was significantly greater during grasps 3 and 4 in the neurofeedback (p = 0.049 and p < 0.001, respectively) than in the sham. There was a significant difference of mu suppression between grasps 1 and 4 in the neurofeedback (p = 0.014), whereas there was no difference in the sham. For the contralesional motor cortex, ANOVA identified no significant factors. For the ipsilesional parietal cortex, ANOVA showed that intervention, time, and the interaction between the intervention and time were significant factors (p = 0.007, 0.001, and 0.017, respectively). Mu suppressions during grasps 3 and 4 were significantly greater in the neurofeedback (p = 0.030 and < 0.001, respectively) than in the sham. There were significant differences in mu suppression between grasps 1 and 2, grasps 1 and 3, and grasps 1 and 4 in the neurofeedback (p < 0.001, < 0.001, and 0.024, respectively), whereas there was no such difference in the sham. For the contralesional parietal cortex, ANOVA showed that intervention, time, and the interaction between the intervention and time were significant factors (p = 0.036, 0.010, and 0.032, respectively), and mu suppression during grasp 4 was significantly greater in the neurofeedback (p = 0.003) than in the sham. There was a significant difference of mu suppression between grasps 1 and 4 and grasps 2 and 4 in the neurofeedback (p = 0.018 and p < 0.001, respectively), whereas there was no such difference in the sham.

A moderate positive correlation was observed between the Fugl-Meyer Assessment of the upper extremity and the difference of mu suppression between grasps 1 and 4 in the ipsilesional motor cortex (ρ = 0.527, p = 0.044) (Fig. 4A). There was no significant correlation in the contralesional motor cortex or bilateral parietal cortex.

In this study, we investigated the effects of EEG-based motor imagery neurofeedback designed to enhance the ipsilesional mu suppression in the sensorimotor cortex of patients with subacute stroke. Specifically, we observed that mu suppression in the motor and parietal cortices was significantly greater during the neurofeedback sessions. Furthermore, we found a progressive increase in mu suppression in the ipsilesional motor and bilateral parietal cortices during motor attempts following neurofeedback. Those with lower Fugl-Meyer scores showed greater mu suppression in the ipsilesional motor cortex during the 4th grasp compared to the 1st one. These findings provide important insights into the neurophysiology of neurofeedback training.

Similar to our study, a previous study using neurofeedback with mirror image revealed mu suppression in the ipsilesional motor cortex and parietal lobes of patients with stroke [12]. Another study using functional MRI showed that neurofeedback training increased the laterality of motor cortex activity, leading to improved hand motor performance as well as white matter tract changes [13]. Concordant with this, a meta-analysis showed that a neurofeedback intervention for post-stroke upper limb weakness was associated with the reorganization of cortical activity and was clinically effective [14]. However, most previous studies focused on patients with chronic stroke, which distinguishes our study from those with subacute stroke. Only a few studies such as that of Hashimoto et al., have shown that neurofeedback with a brain-computer interface could be utilized successfully and safely in patients with acute stroke [15]. Considering that appropriate and tailored rehabilitation during the acute and subacute phases may affect long-term functional outcomes [16], more studies on the effects of neurofeedback in subacute patients using EEG are needed in the future.

Previous studies have primarily monitored how mu suppression is observed on EEG during neurofeedback interventions. However, our study is distinguished by examining mu suppression during grasp attempts following neurofeedback. This approach revealed that mu suppression during grasp in the ipsilesional primary motor cortex progressively increased as the neurofeedback of the grasp imagination continued. In operant conditioning, learning happens when a specific brain activity is matched with an external signal and is reinforced when correct responses are rewarded with feedback [17]. In this context, our study showed that motor imaginary neurofeedback might strengthen the ipsilesional sensorimotor loop, as in the study by Miller et al. [18], which could facilitate motor recovery by re-establishing contingency.

Additionally, we observed enhanced mu suppression in both the bilateral parietal cortices and the ipsilesional motor cortex during motor imagery-based neurofeedback, as well as during the subsequent execution of grasp activities. Previous studies have demonstrated that the bilateral parietal cortices play a critical role in motor imagination [19, 20] and is involved in motor intention or attempt [21, 22]. The involvement of the bilateral parietal cortices in both motor imagery and motor attempt underscores its importance in the neural processes underlying motor control. By leveraging the roles of the bilateral parietal cortex in motor functions, neurofeedback interventions can be tailored to reinforce the neural pathways involved in motor recovery, potentially improving the efficacy of rehabilitation strategies patients with stroke.

Interestingly, our study showed that a lower Fugl-Meyer score was associated with greater fourth grasp mu suppression than the first grasp mu suppression, which suggests that individuals with more moderate-to-severe weakness may benefit more from neurofeedback. Previous studies have shown that neurofeedback combined with brain-computer interfaces or robots is feasible in patients with moderate-to-severe stroke, showing promising results [23–25]. Additionally, Kober et al. revealed that patients with stroke exhibited changes in cortical activity on EEG following neurofeedback, which were not observed in a healthy control group [26]. These studies indicate that abnormal cortical activity patterns after stroke can be normalized through neurofeedback in patients with moderate-to-severe weakness. Conventional rehabilitation is usually based on the repetitive training of volitional and task-specific movements. Therefore, patients with severe paralysis often experience challenges in eliciting responses or performing the necessary movements. Considering these limitations, the use of neurofeedback with conventional rehabilitation holds promise for providing more effective and personalized rehabilitation treatments. This study has a few limitations. First, the neurofeedback intervention was briefly provided and was insufficient to elicit clinically meaningful effects. Furthermore, because videos of other patients undergoing neurofeedback interventions were used as sham, it is possible that the patients believed that they were receiving neurofeedback, which could have caused the sham to act as an active control. After the neurofeedback and sham interventions, no separate interviews were conducted to ask the patients whether they believed that they had received neurofeedback or sham. Although the application of this sham intervention may have reduced the observable differences between neurofeedback and sham, this study found that mu suppression measured during grasping after neurofeedback was significantly different from sham in the ipsilesional motor cortex and bilateral parietal cortices. Finally, our study included a small number of participants with subacute stroke. Future research should involve the provision of neurofeedback to a larger cohort of patients over an extended period to evaluate its long-term effects.

In our study, neurofeedback elicited significant mu suppression in the ipsilesional hemisphere of subacute stroke patients, which persisted during subsequent grasp tasks. This indicates that neurofeedback enhances motor-related cortical activity during real motor attempts, making it a promising addition to stroke rehabilitation. Our study also highlights the potential of neurofeedback in normalizing abnormal brain activity and strengthening the ipsilesional sensorimotor loop. Future research with more patients and long-term follow-ups would be valuable in understanding its benefits and mechanisms.

ANOVA Analysis of variance

CI Confidence interval

ERD Event-related desynchronization

MRI Magnetic resonance imaging

EEG Electroencephalography

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Keimyung University Dongsan Hospital (IRB number 2021-04-112-003). All participants signed the written consent form.

Availability of data and materials

Pseudonymized data supporting the findings of this study are available from the corresponding author, Prof. Won-Seok Kim, upon reasonable request, subject to approval by the local IRB, and completion of a legal data-sharing agreement.

Competing interests

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.NRF-2022R1A2C1006046, RS-2024-00397674 & No.NRF-2022R1A2B5B01001443).

Authors’ contributions

S.C. primarily contributed to the data interpretation, analysis, and drafting of the manuscript. K.T.K. was responsible for the design of the study, the acquisition of data through patient recruitment, and drafting of the manusript. W.S.K. was responsible for the study conception, design of the work, funding acquisition, and study management. J.K. was responsible for study conception, design of the work, organization of the data, supervision of analysis, and funding acquisition. W.K.C., N.J.P., and J.S.C. contributed to the interpretation of the data. H.L. was involved in the analysis of the data. All authors reviewed, edited, and approved the final manuscript.

Acknowledgements

None

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Table 1. Characteristics of included patients with stroke.

Number	Age	Sex	Onset (days)	Laterality	Lesion	mRS	FMA-UE	MMSE	MBI
1	80	Male	12	Right	Frontal	3	18	24	81
2	60	Male	15	Left	Corona radiata	3	66	29	80
3	56	Male	26	Right	Basal ganglia	4	7	25	15
4	82	Male	15	Right	Pons	4	61	17	59
5	79	Male	14	Left	Corona radiata	3	64	26	58
6	69	Male	13	Left	Corona radiata	4	46	28	38
7	49	Male	24	Right	Corona radiata	3	64	30	95
8	82	Female	9	Left	Corona radiata & Basal ganglia	4	51	19	37
9	53	Male	14	Right	Corona radiata	2	59	29	89
10	69	Male	12	Left	Thalamus	3	62	23	53
11	75	Male	19	Left	Medulla	3	64	23	44
12	40	Male	31	Left	Fronto-temporal & Temporo-occipital	2	65	25	97
13	83	Male	13	Right	Thalamus	4	60	22	38
14	50	Male	8	Right	Corona radiata & Basal ganglia	3	58	29	48
15	71	Female	24	Right	Pons	4	30	17	19

FMA-UE, Fugl-Meyer Assessment of the upper extremity, MBI, modified Barthel Index; MMSE, Mini-Mental State Examination; mRS, modified Rankin Scale.

No competing interests reported.

Effect of Electroencephalography-based Motor Imagery Neurofeedback on Mu Suppression During Motor Attempt in Patients with Stroke

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusion

Figures

Background

Methods

Participants

Study design

Neurofeedback mechanism

EEG acquisition

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Status:

Version 1