This study was supported by the National Science Foundation of Guangdong Province (No.2023A1515010586), Guangzhou clinical characteristic technology construction project (2023C-TS19), Education Science Planning Project of Guangdong Province (No.2022GXJK299), the General Guidance Program of Guangzhou Municipal Health and Family Planning (20221A011109, 20231A011111), 2022 Guangzhou Higher Education Teaching Quality and Teaching Reform Project of Higher Education Teaching reform General project (No.2022JXGG088/02-408-2306040XM), 2022 Guangzhou Medical University Student Innovation Ability Improvement Plan project (No.PX-66221494/02-408-2304-19062XM), 2021 school-level education science planning project (2021: NO.45), 2023 First-class Undergraduate Major Construction Fund of high-level University (2022JXA009,&#160;2022JXD001, 2022JXD003)/(02-408-2304-06XM), Guangzhou Education Bureau university research project (No. 202235384), 2022 Undergraduate Teaching Quality and Teaching Reform Project of Guangzhou Medical University (2022 NO. 33), National Science Foundation of Guangdong Province (No. 2021A1515012197), and Guangzhou and University Foundation (No. 202102010100).

This project was approved by the Medical Ethics Association of the Fifth Affiliated Hospital of Guangzhou Medical University (approval No. KY01-2021-05-01) on August 19, 2021. The trial was registered in the Chinese Clinical Trials Registry (registration number: NO. ChiCTR2100050162) on August 19, 2021. All patients signed the informed consent form.
1. Recruitment
<ol>
	<li>Inclusion criteria
	<ol>
		<li>Recruit patients who meet the diagnostic criteria of stroke formulated by the fourth National Conference on Cerebrovascular Disease; have a confirmed cerebral apoplexy by brain computed tomography or magnetic resonance imaging with the disease course &#8805; 6 months; had a stroke leading to one hand dysfunction at Brunnstrom stage 1-3; have a Montreal cognitive assessment (MoCA) &#8805; 26; aged from 50 to 75 years; right-handedness prevails; have voluntarily signed the informed consent.</li>
	</ol>
	</li>
	<li>Exclusion criteria
	<ol>
		<li>Exclude patients with previous history of upper limb fracture, deformity of upper limb, etc.; their condition is unstable or changes occur during the experiment, which affects the experimental results; unable to complete the experiment due to serious heart, lung, liver and kidney and other important organ diseases; severe cognitive dysfunction or aphasia; suffering from serious mental disorders; taking medications that reduce the threshold of seizures; have any implanted device or metal that may be affected by the magnetic field generated by transcranial magnetic stimulation (TMS).</li>
	</ol>
	</li>
</ol>
2. MI-BCI training
NOTE: The rehabilitation robot for upper limb hand function based on motor imagery is selected in this study. The device comprises an electroencephalogram (EEG) cap (Figure 1A), a computer terminal (i.e., control interface; Figure 1B), an external manipulator (Figure 1E), and a 23 inch computer display screen (Figure 1C).
<ol>
	<li>Preparation of subjects
	<ol>
		<li>Explain the training purpose and method and note these for the subjects.</li>
		<li>Put the MI-BCI EEG cap on the subject to ensure that the Cz point of the subject&#39;s head coincides with the Cz point of the cap9,10. The subject&#39;s Cz point is the vertex of the head center. To confirm this, check the intersection point of the ear bead line and the human median line passing through the nose and eyebrow center. Determine the Cz point of the EEG cap according to the international 10-20 EEG positioning system11.</li>
		<li>Keep the ears exposed from the ear seam of the head cap, and adjust the chin strap to fix the head cap.</li>
		<li>Insert 24 electrodes dipped in normal saline into the groove of the EEG cap and clip 2 reference electrodes into the two ear lobes. 
		NOTE: The electrode should be kept in a wet state without dripping. If the electrode is too dry, the EEG signal acquisition process will be affected. If the electrode is too wet, the dripping saline might affect the functional performance of the subject.</li>
		<li>As the operator, pay attention to EEG acquisition during the training. If there is a small range of EEG disturbance, first consider whether the corresponding electrode shows dryness, water shortage, or other conditions with the training. At this time, stop the training and EEG acquisition immediately and properly wet the electrode before the training continues.</li>
		<li>If the EEG signal has an extensive range of disturbance, first consider whether the reference electrode has fallen off or not. At this time, stop the training immediately and clamp the reference electrode at the earlobe again.</li>
		<li>Put the manipulator on the patient and adjust it to a comfortable training position to prevent upper limb forearm pain caused by over-tightening or slipping.</li>
	</ol>
	</li>
	<li>Software operation
	<ol>
		<li>Open the training software MI-BCI upper limb hand function rehabilitation robot, click the User List, and enter the patient information including name, disease name, date of birth, date of illness, and location of the affected side.</li>
		<li>Adjust the stability of the EEG signal to no obvious clutter.</li>
		<li>Click the Resting EEG button to let the patient complete the resting EEG acquisition process according to the voice and text prompts: Please close your eyes to relax and please open your eyes. This lasts for 60 s.</li>
		<li>Click the Task Setting button to set the task difficulty and training duration. Set the initial training difficulty upward or downward from level 9 according to the actual situation of the patients, and set the training duration to 30 min.</li>
		<li>During the training, the MI-BCI system will automatically adjust the task difficulty according to the patient&#39;s performance. Check the difficulty of training as reflected by the size of the objects grasped; the higher the difficulty of the task, the finer the objects grasped.</li>
		<li>Click the Task EEG button to start the formal training. The screen on the patient side displays: Please close your eyes and relax for 5s.</li>
		<li>After 5 s, the screen on the patient side displays: Please open your eyes and imagine your hand open/make a fist, which lasts for 3 s. Ask the patient to follow the on-screen commands.</li>
		<li>The screen on the patient side displays the gripping video/opening video, to assist the patient to simulate the actions demonstrated by the video in imagination. Ask the patient to perform this motion imagination task that lasts for 5 s.</li>
		<li>The system extracts the EEG signal of the patient&#39;s motor imagination, uses the algorithm to analyze the score of the motor intention, and displays it on the screen of the patient&#39;s side. Check the robot feedback, that will be displayed in 4 s. 
		NOTE: The model adopted by the MI-BCI system is FBCNet (multi-band filtering + channel separation convolution + variance layer +FC) to extract features and score EEG signals12. The threshold value of the MI-BCI system is determined based on a large number of clinical trial data and core EEG algorithm. The threshold value is 60 points.</li>
		<li>Check the motor intention displayed. If the patient&#39;s motor intention is &#8805; 60 points (threshold value), the system recognizes that the specified action has been achieved. If the patient&#39;s intention to move &#60; 60 points, and the system determines that the patient cannot perform the specified movement, ensure that the audio prompts the patient to keep going and not get discouraged and the patient complies to this.</li>
		<li>During the training process, observe the EEG waveform in real time. If there is any abnormal situation, suspend the training in time, and restart the training after the signal is adjusted to be stable. If the patient has pain or discomfort during the training, stop the training and record the reason for termination.</li>
	</ol>
	</li>
</ol>
3. Clinical evaluation
<ol>
	<li>Information collection
	<ol>
		<li>Collect and record basic personal information about the subjects, such as name, sex, date of birth, education level, etc.</li>
		<li>Complete the collection of the subject&#39;s medical history, such as clinical history, medication history, personal history, etc.</li>
	</ol>
	</li>
	<li>Motor function evaluation
	<ol>
		<li>Evaluate the Fugl-Meyer assessment of upper extremity (FMA-UE)13 on stroke subjects. 
		The total score is 66. The higher the score, the better the patient&#39;s functional performance.</li>
		<li>Evaluate the Wolf motor function test (WMFT)14 on stroke subjects. The total score is 85. The higher the score, the better the patient&#39;s functional performance.</li>
	</ol>
	</li>
	<li>Cognitive function assessment
	<ol>
		<li>Evaluate the mini-mental state examination (MMSE)15 on stroke subjects. According to the educational level, divide the score into illiterate &#8804;17, education level of primary school &#8804;20, and education level of junior high school and above &#8804;24.</li>
	</ol>
	</li>
	<li>Emotional function assessment
	<ol>
		<li>Evaluate the Hamilton anxiety scale (HAMA)16 on stroke subjects. The score ranges are: &#8805;21, there is evident anxiety; &#8805;14 points, definitely have anxiety; and more than 7 points, might have anxiety. A score of less than 7 indicates no symptoms of anxiety.</li>
		<li>Evaluate the Hamilton depression scale (HAMD)17 on stroke subjects. A score of &#60; 7 is average, a score of 7-17 indicates that the individual might have depression, a score of 17-24 indicates that the individual can be diagnosed with depression, and a score of &#62;24 indicates that the individual has severe depression.</li>
	</ol>
	</li>
</ol>
4. fNIRS brain function assessment
For this study, place 10 sources and 12 detectors on the fNIRS test head caps to correspond to the eight regions of interest (ROIs) in this study, including the bilateral dorsolateral prefrontal cortex (DLPFC), the dorsolateral promoter cortex (PMC), the dorsolateral primary motor cortex (M1), and the dorsolateral primary sensory cortex (S1; Figure 2).
<ol>
	<li>Preparation of subject
	<ol>
		<li>Explain the training purpose and method and note them for the subjects. Ensure that the subjects understand the experiment and are familiar with the relevant procedures; this helps improve patient compliance.</li>
		<li>Put the fNIRS test head cap on the subject, determine the Cz point of the subject&#39;s head, and ensure that it coincides with the Cz point of the head cap as described in step 2.1.2.</li>
		<li>Adjust the head cap for the subject to ensure that both ears are exposed from the ear seam of the cap, the bilateral ear edges of the cap are naturally fitted to the bilateral mastoid process, the leading edge of the cap is naturally fitted to the forehead, and the trailing edge is naturally fitted to the posterior occipital part. Adjust the chin band to fix the cap.</li>
		<li>Adjust the tightness of the jaw band appropriately. If it is too loose, it will lead to the displacement of the head cap during the experiment, which will result in inaccurate positioning and affect signal acquisition. If it is too tight, it will cause discomfort to the subject, which will reduce compliance and affect the functional performance of the subject.</li>
	</ol>
	</li>
	<li>Pre-collection and collection of data
	<ol>
		<li>Enter the fNIRS control software, select the Experimental Subject, and establish patient treatment files according to the patient information.</li>
		<li>Click the Pre-collection button to calibrate the signals. According to the signal intensity of the site displayed by the near-infrared functional brain imaging system, improve the signal intensity and stability of the site by adjusting the light source or receiver of the head cap so that it is closer to the subject&#39;s scalp.</li>
		<li>When the system displays all sites as green, the signal strength is stable. Stop the pre-collection and click the Automatic gain button to make the final adjustment to the signal. 
		&#8203;NOTE: In the pre-collection process, the near-infrared brain functional imaging system presents different colors to represent the signal strengths of sites. Gray represents low signal strength, yellow represents good signal strength, green represents excellent signal strength, and red represents over-strong signal strength.</li>
		<li>Click the Start button to collect the signals. Observe and record various conditions in the experiment, such as signal fluctuations and poor electrode contact.</li>
	</ol>
	</li>
	<li>fNIRS-motor task assessment
	<ol>
		<li>Select the Motor Task paradigm in the fNIRS system.</li>
		<li>Place the patient&#39;s upper limb on the test table and ask the patient to rest for 10 s before the experiment.</li>
		<li>Ask the patient to follow the exercise rhythm to grasp the affected hand in three blocks during the experiment, each block includes 30 s task and 30 s rest. Each task consists of 15 trials, each trial includes 1 s gripping and 1 s opening of the grasp.</li>
		<li>During each rest, ask the patients to close their eyes and rest. If the subject is unable to grasp the affected hand, ask the subject to perform an exercise of motor imagery. The test lasts for 190 s (Figure 3). After all three blocks are complete, end the task, save the data, and import it to the self-built database.</li>
	</ol>
	</li>
	<li>fNIRS-cognitive task assessment (Stroop task)
	<ol>
		<li>Run behavioral research software and choose the Cognitive Task paradigm. Select the patient treatment files and then select the Congruence Test.</li>
		<li>Ask the subject to place the healthy hand on the button of the keyboard, then ask the subject to rest for 10 s before the trial starts. Perform three blocks of the congruence test, each block includes 60 s task and 30 s rest. Each task consists of 10 trials, each consisting of 2000 ms fixation and 4000 ms stimulus-response with a total duration of 280 s (Figure 4).
		<ol>
			<li>When left symbol is displayed on the left of the field font, press the &#8592; button on the keyboard as soon as possible according to the character meaning (that is, left).</li>
			<li>When right symbol is displayed on the left of the field grid, click the &#8594; button on the keyboard as soon as possible according to the meaning of the character (i.e., right).</li>
		</ol>
		</li>
		<li>Select the Incongruence Test; the procedure is the same as that of the congruence test.
		<ol>
			<li>When right symbol is displayed on the left of the field font, ignore the character meaning and press the &#8592; button on the keyboard as soon as possible according to the position where the text appears (i.e., left).</li>
			<li>When left symbol is displayed on the right side of the field font, ignore the character meaning and click the &#8594; button on the keyboard as soon as possible according to the position of the text (i.e., right).</li>
		</ol>
		</li>
		<li>Complete the task, save the data, and export it to the self-created database.</li>
	</ol>
	</li>
</ol>
5. Post-treatment
<ol>
	<li>Perform the evaluation as done for the clinical function evaluation and fNIRS brain function evaluation in the pre-treatment. Perform all the evaluations after the end of the 10th training session (see steps 3.1 to 4.4 for details).</li>
</ol>
6. Data processing and analysis
<ol>
	<li>Summarize and analyze patients&#39; personal information and clinical assessment scale data.</li>
	<li>Use a commercial software to preprocess near-infrared data. Perform elimination of time interval and motion artifacts of interference tests, selection of a bandpass filter (0.01-0.2 Hz) to remove noise, calculation of the relative changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) according to the modified Beer-Lambert law and convert the optical density signal into a blood oxygen concentration signal. 
	NOTE: HbO is more sensitive to changes between different conditions than HbR, so only HbO data is used in this study scheme for subsequent analysis.</li>
	<li>Select HbO in blood oxygen type as analysis data. Set up the GLM design matrix and select the task phase in the task. Click the Estimate button to fit the established design matrix with the collected data.</li>
	<li>Use the established linear correlation model to calculate the beta value in each ROI. Export the parameters, accuracy rate (ACC) and response time (RT), in the Stroop task through the behavioral research software.</li>
</ol>

Restoring Post-Stroke Motor Function by Motor Imagery Brain-Computer Interface

<ol>
	<li>Dawson, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Vagus+nerve+stimulation+paired+with+rehabilitation+for+upper+limb+motor+function+after+ischaemic+stroke+(VNS-REHAB):+a+randomised,+blinded,+pivotal,+device+trial.">Vagus nerve stimulation paired with rehabilitation for upper limb motor function after ischaemic stroke (VNS-REHAB): a randomised, blinded, pivotal, device trial.</a> Lancet. 397 (10284), 1545-1553 (2021).</li><li>Lin, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Frequency+Effect+of+the+Motor+Imagery+Brain+Computer+Interface+Training+on+Cortical+Response+in+Healthy+Subjects:+A+Randomized+Clinical+Trial+of+Functional+Near-Infrared+Spectroscopy+Study.">The Frequency Effect of the Motor Imagery Brain Computer Interface Training on Cortical Response in Healthy Subjects: A Randomized Clinical Trial of Functional Near-Infrared Spectroscopy Study.</a> Frontiers in Neuroscience. 16, 810553 (2022).</li><li>Carino-Escobar, R. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Longitudinal+Analysis+of+Stroke+Patients'+Brain+Rhythms+during+an+Intervention+with+a+Brain-Computer+Interface.">Longitudinal Analysis of Stroke Patients' Brain Rhythms during an Intervention with a Brain-Computer Interface.</a> Neural Plasticity. 2019, 7084618 (2019).</li><li>Mane, R., Chouhan, T., Guan, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BCI+for+stroke+rehabilitation:+motor+and+beyond.">BCI for stroke rehabilitation: motor and beyond.</a> Journal of Neural Engineering. 17 (4), 041001 (2020).</li><li>Khan, M. A., Das, R., Iversen, H. K., Puthusserypady, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+on+motor+imagery+based+BCI+systems+for+upper+limb+post-stroke+neurorehabilitation:+From+designing+to+application.">Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application.</a> Computers In Biology And Medicine. 123, 103843 (2020).</li><li>Hendricks, H. T., van Limbeek, J., Geurts, A. C., Zwarts, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motor+recovery+after+stroke:+a+systematic+review+of+the+literature.">Motor recovery after stroke: a systematic review of the literature.</a> Archives of Physical Medicine and Rehabilitation. 83 (11), 1629-1637 (2002).</li><li>Cheng, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-Computer+Interface-Based+Soft+Robotic+Glove+Rehabilitation+for+Stroke.">Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke.</a> IEEE Transactions on Biomedical Engineering. 67 (12), 3339-3351 (2020).</li><li>Ang, K. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-computer+interface-based+robotic+end+effector+system+for+wrist+and+hand+rehabilitation:+results+of+a+three-armed+randomized+controlled+trial+for+chronic+stroke.">Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke.</a> Frontiers in Neuroengineering. 7, 30 (2014).</li><li>Nuwer, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IFCN+standards+for+digital+recording+of+clinical+EEG.+The+International+Federation+of+Clinical+Neurophysiology.">IFCN standards for digital recording of clinical EEG. The International Federation of Clinical Neurophysiology.</a> Electroencephalography and Clinical Neurophysiology. 52, 11-14 (1999).</li><li>Klem, G. H., L&#252;ders, H. O., Jasper, H. H., Elger, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ten-twenty+electrode+system+of+the+International+Federation.+The+International+Federation+of+Clinical+Neurophysiology.">The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology.</a> Electroencephalography and Clinical Neurophysiology. 52, 3-6 (1999).</li><li>Uwe Herwig, ., Peyman Satrapi, ., Sch&#246;nfeldt-Lecuona, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+the+international+10-20+EEG+system+for+positioning+of+transcranial+magnetic+stimulation.">Using the international 10-20 EEG system for positioning of transcranial magnetic stimulation.</a> Brain Topography. , (2003).</li><li>Mane, R., Robinson, N., Vinod, A. P., Lee, S. W., Guan, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Multi-view+CNN+with+Novel+Variance+Layer+for+Motor+Imagery+Brain+Computer+Interface.">A Multi-view CNN with Novel Variance Layer for Motor Imagery Brain Computer Interface.</a> Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2020, 2950-2953 (2020).</li><li>Sanford, J., Moreland, J., Swanson, L. R., Stratford, P. W., Gowland, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reliability+of+the+Fugl-Meyer+assessment+for+testing+motor+performance+in+patients+following+stroke.">Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke.</a> Physical Therapy. 73 (7), 447-454 (1993).</li><li>Martinez, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Reaching+Performance+Scale+for+2+Wolf+Motor+Function+Test+Items.">A Reaching Performance Scale for 2 Wolf Motor Function Test Items.</a> Archives of Physical Medicine and Rehabilitation. 101 (11), 2015-2026 (2020).</li><li>Dufouil, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Population+norms+for+the+MMSE+in+the+very+old:+estimates+based+on+longitudinal+data.+Mini-Mental+State+Examination.">Population norms for the MMSE in the very old: estimates based on longitudinal data. Mini-Mental State Examination.</a> Neurology. 55 (11), 1609-1613 (2000).</li><li>Thompson, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hamilton+Rating+Scale+for+Anxiety+(HAM-A).">Hamilton Rating Scale for Anxiety (HAM-A).</a> Occupational Medicine. 65 (7), 601 (2015).</li><li>Zimmerman, M., Martinez, J. H., Young, D., Chelminski, I., Dalrymple, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Severity+classification+on+the+Hamilton+Depression+Rating+Scale.">Severity classification on the Hamilton Depression Rating Scale.</a> Journal of Affective Disorders. 150 (2), 384-388 (2013).</li><li>Bai, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Different+Therapeutic+Effects+of+Transcranial+Direct+Current+Stimulation+on+Upper+and+Lower+Limb+Recovery+of+Stroke+Patients+with+Motor+Dysfunction:+A+Meta-Analysis.">Different Therapeutic Effects of Transcranial Direct Current Stimulation on Upper and Lower Limb Recovery of Stroke Patients with Motor Dysfunction: A Meta-Analysis.</a> Neural Plasticity. 2019, 1372138 (2019).</li><li>Dimyan, M. A., Cohen, L. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuroplasticity+in+the+context+of+motor+rehabilitation+after+stroke.">Neuroplasticity in the context of motor rehabilitation after stroke.</a> Nature Reviews Neurology. 7 (2), 76-85 (2011).</li><li>Bai, Z., Fong, K. N. K., Zhang, J. J., Chan, J., Ting, K. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immediate+and+long-term+effects+of+BCI-based+rehabilitation+of+the+upper+extremity+after+stroke:+a+systematic+review+and+meta-analysis.">Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis.</a> Journal of NeuroEngineering and Rehabilitation. 17 (1), 57 (2020).</li><li>Yang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Effect+of+Brain-Computer+Interface+Training+on+Rehabilitation+of+Upper+Limb+Dysfunction+After+Stroke:+A+Meta-Analysis+of+Randomized+Controlled+Trials.">The Effect of Brain-Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials.</a> Frontiers in Neuroscience. 15, 766879 (2021).</li><li>Pandian, S., Arya, K. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stroke-related+motor+outcome+measures:+do+they+quantify+the+neurophysiological+aspects+of+upper+extremity+recovery.">Stroke-related motor outcome measures: do they quantify the neurophysiological aspects of upper extremity recovery.</a> Journal of Bodywork and Movement Therapies. 18 (3), 412-423 (2014).</li><li>Potter, S. M., El Hady, A., Fetz, E. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Closed-loop+neuroscience+and+neuroengineering.">Closed-loop neuroscience and neuroengineering.</a> Frontiers in Neural Circuits. 8, 115 (2014).</li><li>Nowak, D. A., Grefkes, C., Ameli, M., Fink, G. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interhemispheric+competition+after+stroke:+brain+stimulation+to+enhance+recovery+of+function+of+the+affected+hand.">Interhemispheric competition after stroke: brain stimulation to enhance recovery of function of the affected hand.</a> Neurorehabilitation and Neural Repair. 23 (7), 641-656 (2009).</li></ol>

The authors have nothing to disclose.

The rehabilitation period for moderate and severe upper limb motor dysfunction after stroke is long and the recovery is difficult, which has always been the focus of clinical rehabilitation research18. Traditional upper limb rehabilitation training is mostly simple peripheral intervention or central intervention19. Meanwhile, due to the lack of active participation of patients with moderate and severe limb dysfunction, passive treatment is mainly used, with poor rehabilitat...

An erratum was issued for:&#160;<a href="https://www.jove.com/t/65405">Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke</a>. The Authors section was updated from:
Yongchun Jiang1,2,3 
Junxiao Yin4 
Biyi Zhao1,3,5 
Yajie Zhang1,3 
Tingting Peng1,3 
Wanqi Zhuang1,3 
Siqing Wang1,3 
Siqi Huang1,3 
Meilian Zhong1,2,3 
Yanni Zhang1,3 
Guibing Tang1,3 
Bingchi Shen6 
Haining Ou1,3 
Yuxin Zheng2,3&#160; 
Qiang Lin2,3 
1Guangzhou Medical University 
2Department of Rehabilitation Medicine, The Seventh Affiliated Hospital of Sun Yat-sen University 
3Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Guangzhou Medical University 
4Clinical Medical College of Acupuncture and Rehabilitation, Guangzhou University of Traditional Chinese Medicine 
5School of Traditional Chinese Medicine, Jinan University 
6Department of Stomatology, Second Clinical Medical College, Dongguan Campus of Guangdong Medical University 
&#160;
to:
Yongchun Jiang1,2,3 
Junxiao Yin4 
Biyi Zhao1,3,5 
Yajie Zhang1,3 
Tingting Peng1,3 
Wanqi Zhuang1,3 
Siqing Wang1,3 
Siqi Huang1,3 
Meilian Zhong1,3 
Yanni Zhang1,3 
Guibing Tang1,3 
Bingchi Shen6 
Haining Ou1,3 
Yuxin Zheng2,3&#160; 
Qiang Lin2,3 
1Guangzhou Medical University 
2Department of Rehabilitation Medicine, The Seventh Affiliated Hospital of Sun Yat-sen University 
3Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Guangzhou Medical University 
4Clinical Medical College of Acupuncture and Rehabilitation, Guangzhou University of Traditional Chinese Medicine 
5School of Traditional Chinese Medicine, Jinan University 
6Department of Stomatology, Second Clinical Medical College, Dongguan Campus of Guangdong Medical University 
&#160;

An erratum was issued for:&#160;<a href="https://www.jove.com/t/65405">Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke</a>. The Authors section was updated.

Erratum: Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

Nearly 85% of stroke patients have motor dysfunction1, especially due to the limited rehabilitation effect of patients with moderate and severe upper limb motor dysfunction, which seriously affects patients' ability to live independent daily lives and has been the focus and difficulty of research. Non-invasive brain-computer interface (BCI) is known as an emerging treatment for rehabilitation of motor dysfunction after stroke2. BCI is the direct conversion of the sensory perception, imagery, cognition, and thinking of users or subjects into actions, without reliance on peripheral nerves or muscles, to establish direct communication and control channels between the brain and external devices3. At present, the paradigms of BCI for clinical rehabilitation include motor imagery (MI), steady-state visual evoked potentials (SSVEP), and auditory evoked potentials (AEP) P3004, of which the most commonly used and convenient is motor imagery brain-computer interface (MI-BCI). MI is an intervention that uses visual/kinesthetic motor imagery to visualize the execution of motor tasks (such as hand, arm, or foot movements). On the one hand, previous studies have demonstrated that activation of the associated motor cortex during MI is similar to actual motor execution5. On the other hand, unlike other paradigms, MI can activate a specific area of activity through motor memory without any external stimulus to improve motor function; this is conducive to implementation in stroke patients, especially when combined with hearing dysfunction6.
Moreover, MI-BCI has been shown to have a positive effect on improving motor dysfunction in stroke patients. Cheng et al. reported that compared with simple soft robotic glove intervention, the soft robotic glove based on MI-BCI combined with tasks oriented to daily life activities showed more obvious functional improvement and more lasting kinesthetic experience in chronic stroke patients after 6 weeks of intervention. Furthermore, it was also able to elicit the perception of motor movements7. Additionally, Ang et al. included 21 chronic stroke patients with moderate to severe upper limb dysfunction for randomized intervention. The clinical function was evaluated before and after intervention by the Fugl-Meyer assessment of upper extremity (FMA-UE). The results showed that, compared with simple haptic knob (HK) robot intervention (HK group) and standard arm therapy intervention (SAT group), the motion gain effect of HK based on MI-BCI intervention (BCI-HK group) was significantly better than that of the other two groups8. However, the specific operation of MI-BCI still requires normative standards, and the mechanism of neural remodeling must be fully understood, which limits the clinical application and promotion of MI-BCI. Therefore, by showing the intervention process of MI-BCI in a 36-year-old male stroke patient with upper limb motor dysfunction, this study will summarize the functional outcome changes and brain function remodeling before and after the intervention to demonstrate the complete operation process of MI-BCI and provide ideas and references for clinical rehabilitation application and mechanism research.

<table><tbody><tr><td>MI-BCI</td><td>Rui Han, China</td><td>RuiHan Bangde</td><td>NA</td></tr><tr><td>E-Prime&amp;nbsp;</td><td></td><td>version 3.0</td><td>behavioral research software.</td></tr><tr><td>fNIRS</td><td>Hui Chuang, China</td><td>NirSmart-500</td><td>NA</td></tr><tr><td>NirSpark</td><td></td><td></td><td>preprocess near-infrared data</td></tr></tbody></table>

motor imagery brain-computer interface in rehabilitation of upper limb motor dysfunction after stroke

The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research owing to the difficulties encountered. Brain-computer interface (BCI) represents a hot frontier technology in brain neuroscience research. It refers to the direct conversion of the sensory perception, imagery, cognition, and thinking of users or subjects into actions, without reliance on peripheral nerves or muscles, to establish direct communication and control channels between the brain and external devices. Motor imagery brain-computer interface (MI-BCI) is the most common clinical application of rehabilitation as a non-invasive means of rehabilitation. Previous clinical studies have confirmed that MI-BCI positively improves motor dysfunction in patients after stroke. However, there is a lack of clinical operation demonstration. To that end, this study describes in detail the treatment of MI-BCI for patients with moderate and severe upper limb dysfunction after stroke and shows the intervention effect of MI-BCI through clinical function evaluation and brain function evaluation results, thereby providing ideas and references for clinical rehabilitation application and mechanism research.

The study presents the clinical function and remodeling of brain function before and after MI-BCI intervention in a 36-year-old male stroke patient. More than 4 months after cerebral hemorrhage, the imaging results showed chronic bleeding focus in the right frontal lobe and the right basal ganglia region-radiative crown region. The patient was diagnosed with left limb motor dysfunction during convalescence from a cerebral hemorrhage. Simple outpatient treatment of MI-BCI was performed in the hospital for 10 days (30 min/...

Watch this Scientific Journal Video about Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke at JoVE.com

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

The purpose of this study is to provide an important reference for the standard clinical operation of motor imagery brain-computer interface (MI-BCI) for upper limb motor dysfunction after stroke.

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research ...

motor-imagery-brain-computer-interface-rehabilitation-upper-limb

Research

JoVE Journal

Neuroscience

1.1K Views.  Guangzhou Medical University. Deze studie richt zich op de klinische toepassing van MI-BCI bij patiënten met een beroerte met matige tot ernstige motorische disfunctie van de bovenste ledematen. En het biedt ideeën en referenties voor het gestandaardiseerde klinische operatie- en mechanismeonderzoek door het operatieproces en het interventie-effect van MI-BCI aan te tonen. MI-BCI heeft een positief effect op het verbeteren van motorische disfunctie bij patiënten met een beroerte.In de toekomst zal echter meer kl...

Video: Auteur in de schijnwerpers: motorische beelden gebruiken Hersen-computerinterface om de motorische en cognitieve functie bij patiënten met een beroerte te verbeteren

Measuring Motor Coordination in Mice

Mice are increasingly being used in behavioral neuroscience, largely replacing rats as the behaviorist's animal of choice. Before aspects of behavior such as emotionality or cognition can be assessed, however, it is vital to determine whether the motor capabilities of e.g. a mutant or lesioned mouse allow such an assessment. Performance on a maze task requiring strength and coordination, such as the Morris water maze, might well be impaired in a mouse by motor, rather than cognitive, impairments, so it is essential to selectively dissect the latter from the former. For example, sensorimotor impairments caused by NMDA antagonists have been shown to impair water maze performance2. 
Motor coordination has traditionally been assessed in mice and rats by the rotarod test, in which the animal is placed on a horizontal rod that rotates about its long axis; the animal must walk forwards to remain upright and not fall off. Both set speed and accelerating versions of the rotarod are available.
The other three tests described in this article (horizontal bar, static rods and parallel bars) all measure coordination on static apparatus. The horizontal bar also requires strength for adequate performance, particularly of the forelimbs as the mouse initially grips the bar just with the front paws. 
Adult rats do not perform well on tests such as the static rods and parallel bars (personal observations); they appear less well coordinated than mice. I have only tested male rats, however, and male mice seem generally less well coordinated than females. Mice appear to have a higher strength:weight ratio than rats; the Latin name, Mus musculus, seems entirely appropriate. The rotarod, the variations of the foot fault test12 or the Catwalk (Noldus)15 apparatus are generally used to assess motor coordination in rats.

Protocols are presented for two established motor coordination tasks, the accelerating rotarod and horizontal bar, also two tests developed in Oxford recently, the static rods and parallel bars. These tests can detect motor impairments potentially of interest in their own right, as well as being possible variables in tests of other areas of behavior.

Mice are increasingly being used in behavioral neuroscience, largely replacing rats as the behaviorist's animal of choice. Before aspects of behavior such ...

Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques1,2,3,4. Recently, we developed a technique to extracellularly stimulate and record individual neurons in Aplysia californica5. We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.
This extracellular technique has advantages. First, extracellular electrodes can stimulate and record neurons through the sheath5, so it does not need to be removed. Thus, neurons will be healthier in extracellular experiments than in intracellular ones. Second, if ganglia are rotated by appropriate pinning of the sheath, extracellular electrodes can access neurons on both sides of the ganglion, which makes it easier and more efficient to identify multiple neurons in the same preparation. Third, extracellular electrodes do not need to penetrate cells, and thus can be easily moved back and forth among neurons, causing less damage to them. This is especially useful when one tries to record multiple neurons during repeating motor patterns that may only persist for minutes. Fourth, extracellular electrodes are more flexible than intracellular ones during muscle movements. Intracellular electrodes may pull out and damage neurons during muscle contractions. In contrast, since extracellular electrodes are gently pressed onto the sheath above neurons, they usually stay above the same neuron during muscle contractions, and thus can be used in more intact preparations.
To uniquely identify motor neurons for a motor pool (in particular, the I1/I3 muscle in Aplysia) using extracellular electrodes, one can use features that do not require intracellular measurements as criteria: soma size and location, axonal projection, and muscle innervation4,6,7. For the particular motor pool used to illustrate the technique, we recorded from buccal nerves 2 and 3 to measure axonal projections, and measured the contraction forces of the I1/I3 muscle to determine the pattern of muscle innervation for the individual motor neurons.
We demonstrate the complete process of first identifying motor neurons using muscle innervation, then characterizing their timing during motor patterns, creating a simplified diagnostic method for rapid identification. The simplified and more rapid diagnostic method is superior for more intact preparations, e.g. in the suspended buccal mass preparation8 or in vivo9. This process can also be applied in other motor pools10,11,12 in Aplysia or in other animal systems2,3,13,14.

Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques1,2,3,4. Recently, we developed a technique to extracellularly stimulate and record individual neurons in Aplysia californica5. We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.

In animals with large  identified neurons (e.g. mollusks), analysis of motor pools is done using  intracellular techniques1,2,3,4. Recently,  we developed ...

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability. Innovative methodologies for restorative neurorehabilitation are urgently required to reduce long-term disability. The ability of the nervous system to reorganize its structure, function and connections as a response to intrinsic or extrinsic stimuli is called neuroplasticity. Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. Beneficial neuroplastic changes may be facilitated with non-invasive electrotherapy, such as neuromuscular electrical stimulation (NMES) and sensory electrical stimulation (SES). NMES involves coordinated electrical stimulation of motor nerves and muscles to activate them with continuous short pulses of electrical current while SES involves stimulation of sensory nerves with electrical current resulting in sensations that vary from barely perceivable to highly unpleasant. Here, active cortical participation in rehabilitation procedures may be facilitated by driving the non-invasive electrotherapy with biosignals (electromyogram (EMG), electroencephalogram (EEG), electrooculogram (EOG)) that represent simultaneous active perception and volitional effort. To achieve this in a resource-poor setting, e.g., in low- and middle-income countries, we present a low-cost human-machine-interface (HMI) by leveraging recent advances in off-the-shelf video game sensor technology. In this paper, we discuss the open-source software interface that integrates low-cost off-the-shelf sensors for visual-auditory biofeedback with non-invasive electrotherapy to assist postural control during balance rehabilitation. We demonstrate the proof-of-concept on healthy volunteers.

A novel low-cost human-machine interface for interactive post-stroke balance rehabilitation system is presented in this article. The system integrates off-the-shelf low-cost sensors towards volitionally driven electrotherapy paradigm. The proof-of-concept software interface is demonstrated on healthy volunteers.

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing ...

The Combined Use of Transcranial Direct Current Stimulation and Robotic Therapy for the Upper Limb

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of daily activities due to lower and upper limb impairments. Intensive physical and occupational therapy are still considered main treatments, but new adjunct therapies to standard rehabilitation that may optimize functional outcomes are being studied.
Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that polarizes underlying brain regions through the application of weak direct currents through electrodes on the scalp, modulating cortical excitability. Increased interest in this technique can be attributed to its low cost, ease of use, and effects on human neural plasticity. Recent research has been performed to determine the clinical potential of tDCS in diverse conditions such as depression, Parkinson&#39;s disease, and motor rehabilitation after stroke. tDCS helps enhance brain plasticity and seems to be a promising technique in rehabilitation programs.
A number of robotic devices have been developed to assist in the rehabilitation of upper limb function after stroke. The rehabilitation of motor deficits is often a long process requiring multidisciplinary approaches for a patient to achieve maximum independence. These devices do not intend to replace manual rehabilitation therapy; instead, they were designed as an additional tool to rehabilitation programs, allowing immediate perception of results and tracking of improvements, thus helping patients to stay motivated.
Both tDSC and robot-assisted therapy are promising add-ons to stroke rehabilitation and target the modulation of brain plasticity, with several reports describing their use to be associated with conventional therapy and the improvement of therapeutic outcomes. However, more recently, some small clinical trials have been developed that describe the associated use of tDCS and robot-assisted therapy in stroke rehabilitation. In this article, we describe the combined methods used in our institute for improving motor performance after stroke.

The combined use of transcranial direct current stimulation and robotic therapy as an add-on for conventional rehabilitation therapy may result in improved therapeutic outcomes due to modulation of brain plasticity. In this article, we describe the combined methods used in our institute for improving motor performance after stroke.

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of ...

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

This article demonstrates complete, detailed procedures for both in vivo bilateral recording and analysis of local field potential (LFP) in the cortical areas of mice, which are useful for evaluating possible laterality deficits, as well as for assessing brain connectivity and coupling of neural network activities in rodents. The pathological mechanisms underlying Alzheimer&#39;s disease (AD), a common neurodegenerative disease, remain largely unknown. Altered brain laterality has been demonstrated in aging people, but whether or not abnormal lateralization is one of the early signs of AD has not been determined. To investigate this, we recorded bilateral LFPs in 3-5-month-old AD model mice, APP/PS1, together with littermate wild type (WT) controls. The LFPs of the left and right secondary motor cortex (M2), specifically in the gamma band, were more synchronized in APP/PS1 mice than in WT controls, suggesting a declined hemispheric asymmetry of bilateral M2 in this AD mouse model. Notably, the recording and data analysis processes are flexible and easy to carry out, and can also be applied to other brain pathways when conducting experiments that focus on neuronal circuits.

We present in vivo electrophysiological recording of the local field potential (LFP) in bilateral secondary motor cortex (M2) of mice, which can be applied to evaluate hemisphere lateralization. The study revealed altered levels of synchronization between the left and right M2 in APP/PS1 mice compared to WT controls.

This article demonstrates complete, detailed procedures for both in vivo bilateral recording and analysis of local field potential (LFP) in the cortical ...

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of sciatic nerve injury rodent models. In this protocol, we describe a novel kinematic analysis method that uses a three-dimensional (3D) motion capture apparatus for functional evaluations using a rat sciatic nerve crush injury model. First, the rat is familiarized with treadmill walking. Markers are then attached to the designated bone landmarks and the rat is made to walk on the treadmill at the desired speed. Meanwhile, the posterior limb movements of the rat are recorded using four cameras. Depending on the software used, marker tracings are created using both automatic and manual modes and the desired data are produced after subtle adjustments. This method of kinematic analysis, which uses a 3D motion capture apparatus, offers numerous advantages, including superior precision and accuracy. Many more parameters can be investigated during the comprehensive functional evaluations. This method has several shortcomings that require consideration: The system is expensive, can be complicated to operate, and may produce data deviations due to skin shifting. Nevertheless, kinematic analysis using a 3D motion capture apparatus is useful for performing functional anterior and posterior limb evaluations. In the future, this method may become increasingly useful for generating accurate assessments of various traumas and diseases.

We introduce a kinematic analysis method that uses a three-dimensional motion capture apparatus containing four cameras and data processing software for performing functional evaluations during fundamental research involving rodent models.

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of ...

Motor Imagery Using VR Digital Avatars in a Brain-Computer Interface System

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

This study introduces an innovative framework for neurological rehabilitation by integrating brain-computer interfaces (BCI) and virtual reality (VR) technologies with the customization of three-dimensional (3D) avatars. Traditional approaches to rehabilitation often fail to fully engage patients, primarily due to their inability to provide a deeply immersive and interactive experience. This research endeavors to fill this gap by utilizing motor imagery (MI) techniques, where participants visualize physical movements without actual execution. This method capitalizes on the brain's neural mechanisms, activating areas involved in movement execution when imagining movements, thereby facilitating the recovery process. The integration of VR's immersive capabilities with the precision of electroencephalography (EEG) to capture and interpret brain activity associated with imagined movements forms the core of this system. Digital Twins in the form of personalized 3D avatars are employed to significantly enhance the sense of immersion within the virtual environment. This heightened sense of embodiment is crucial for effective rehabilitation, aiming to bolster the connection between the patient and their virtual counterpart. By doing so, the system not only aims to improve motor imagery performance but also seeks to provide a more engaging and efficacious rehabilitation experience. Through the real-time application of BCI, the system allows for the direct translation of imagined movements into virtual actions performed by the 3D avatar, offering immediate feedback to the user. This feedback loop is essential for reinforcing the neural pathways involved in motor control and recovery. The ultimate goal of the developed system is to significantly enhance the effectiveness of motor imagery exercises by making them more interactive and responsive to the user's cognitive processes, thereby paving a new path in the field of neurological rehabilitation.

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Motor imagery in a virtual reality environment has wide applications in brain-computer interface systems. This manuscript outlines the use of personalized digital avatars that resemble the participants performing movements imagined by the participant in a virtual reality environment to enhance immersion and a sense of body ownership.

This study introduces an innovative framework for neurological rehabilitation by integrating brain-computer interfaces (BCI) and virtual reality (VR) ...

Bioengineering

Enhancing Stroke Rehabilitation with Virtual Reality-Based Occupational Training

Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System

Stroke rehabilitation often requires frequent and intensive therapy to improve functional recovery. Virtual reality (VR) technology has shown the potential to meet these demands by providing engaging and motivating therapy options. The digital occupational training system is a VR application that utilizes cutting-edge technologies, including multi-touch screens, virtual reality, and human-computer interaction, to offer diverse training techniques for advanced cognitive capacity and hand-eye coordination abilities. The objective of this study was to assess the effectiveness of this program in enhancing cognitive function and upper extremity rehabilitation in stroke patients. The training and assessment consist of five cognitive modules covering perception, attention, memory, logical reasoning, and calculation, along with hand-eye coordination training. This research indicates that after eight weeks of training, the digital occupational training system can significantly improve cognitive function, daily living skills, attention, and self-care abilities in stroke patients. This software can be employed as a time-saving and clinically effective rehabilitation aid to complement traditional one-on-one occupational therapy sessions. In summary, the digital occupational training system shows promise and offers potential financial benefits as a tool to support the functional recovery of stroke patients.

Author Spotlight: Rehabilitation of Stroke Patients With a Digital Occupational Training System

The current protocol outlines how the VR-based digital occupational training system enhances the rehabilitation of patients with cognitive impairment and upper limb dysfunction following a stroke.

Stroke rehabilitation often requires frequent and intensive therapy to improve functional recovery. Virtual reality (VR) technology has shown the ...

Medicine

Integrated Robotic and Conventional Therapy for Post-Stroke Upper Limb Recovery

Enhancing Upper Limb Function and Motor Skills Post-Stroke Through an Upper Limb Rehabilitation Robot

Cerebrovascular accidents, commonly known as strokes, represent a prevalent neurological event leading to significant upper limb disabilities, thereby profoundly affecting individuals' activities of daily living and diminishing their quality of life. Traditional rehabilitation methods for upper limb recovery post-stroke are often hindered by limitations, including therapist and patient fatigue, reliance on singular training methodologies, and lack of sustained motivation. Addressing these challenges, this study introduces an upper limb rehabilitation robot, which uses intelligent feedback motion control to improve therapeutic outcomes. The system is distinguished by its capability to adjust the direction and magnitude of force feedback dynamically, based on the detection of spastic movements during exercises, thereby offering a tailored therapeutic experience. This system is equipped with four distinct training modes, intelligent assessment of joint range of motion, and the ability to personalize training programs. Moreover, it provides an immersive interactive gaming experience coupled with comprehensive safety measures. This multifaceted approach not only elevates the engagement and interest of participants beyond traditional rehabilitation protocols but also demonstrates significant improvements in upper limb functionality and the activities of daily living among hemiplegic patients. The system exemplifies an advanced tool in upper limb rehabilitation, offering a synergistic blend of precision, personalization, and interactive engagement, thereby broadening the therapeutic options available to stroke survivors.

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

This protocol describes an upper limb rehabilitation robot that provides intelligent feedback through four modes. These modes enhance upper limb function and flexibility, thereby improving patients' quality of life.

Cerebrovascular accidents, commonly known as strokes, represent a prevalent neurological event leading to significant upper limb disabilities, thereby ...

Dual Upper Limb Robotic Training in Stroke Rehabilitation

Application of a Dual Upper Limb Task-Oriented Robotic System for the Functional Recovery of the Upper Limb in Stroke Patients

Highly repetitive and task-oriented training has been shown to promote the recovery of limb function in stroke patients. Additionally, bilateral arm training can help stroke survivors regain their upper limb function and improve their daily activities. The dual upper limb task-oriented robotic system is designed to assist the healthy side of the stroke patient in driving the affected side to perform bilateral arm training through the use of a robotic device. It can also guide the patient in carrying out dual upper limb coordinated movements and engage them in a task-oriented virtual game using force feedback and human-computer interaction technology. This study aimed to assess the efficacy of the system in enhancing upper limb function and activities of daily living in stroke patients. The assessment methods used included motor evoked potential (MEP), functional test for the hemiplegic upper extremity-Hong Kong (FTHUE-HK), Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE), and modified Barthel index (MBI). The results of the study indicate that the dual upper limb task-oriented robotic system can significantly improve the corticospinal pathway, upper limb function, and activities of daily living in stroke patients after 6 weeks of treatment. This system can serve as an effective adjunct to upper limb functional rehabilitation in stroke survivors, reducing the dependence on rehabilitation therapists. In conclusion, the dual upper limb task-oriented robotic system provides a new strategy for post-stroke limb functional rehabilitation and holds great potential for application, as it offers certain social and financial benefits.

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

This experimental protocol outlines the use of a dual upper limb task-oriented robotic system for stroke patients with upper limb dysfunction. The findings indicate that this system can significantly improve stroke patients' upper limb function and daily living activities.

Highly repetitive and task-oriented training has been shown to promote the recovery of limb function in stroke patients. Additionally, bilateral arm ...

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

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