Wearable MCI for Stroke
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Northwestern University
Must not be taking: Spasticity treatments
Disqualifiers: Cognitive impairment, Visual impairment, others
No Placebo Group
Trial Summary
What is the purpose of this trial?The purpose of the study is to explore the feasibility of using a wearable device, called a myoelectric-computer interface (MCI), to improve arm movement in people who have had a stroke.
Impaired arm movement after stroke is caused not just by weakness, but also by impaired coordination between joints due to abnormal co-activation of muscles. These abnormal co-activation patterns are thought to be due to abnormal movement planning.The MCI aims to reduce abnormal co-activation by providing feedback about individual muscle activations.
This randomized, controlled, blinded study will test the home use of an MCI in chronic and acute stroke survivors.
Will I have to stop taking my current medications?
The trial protocol does not specify whether you need to stop taking your current medications. However, if you are receiving new spasticity treatments, you may not be eligible to participate.
What data supports the effectiveness of the treatment MCI, Myoelectric-Computer Interface, MyoCI, MCI, Sham MCI, Placebo MCI, Control MCI for stroke?
Research shows that Myoelectric-Computer Interface (MCI) training can help stroke survivors improve arm function by reducing abnormal muscle coactivation, which is when muscles that should work independently contract together. Studies found that stroke survivors who used MCI training showed improvements in arm movement and function, suggesting it could be a promising treatment for stroke rehabilitation.
12345Is the Myoelectric-Computer Interface (MCI) safe for humans?
The studies on Myoelectric-Computer Interface (MCI) training, primarily focused on stroke survivors, suggest that it is generally safe for humans. Participants, including both healthy individuals and those with stroke or spinal cord injury, were able to use the MCI without reported safety issues, and some even showed improvements in muscle function.
12367How does the Myoelectric-Computer Interface (MCI) treatment for stroke differ from other treatments?
The Myoelectric-Computer Interface (MCI) treatment is unique because it uses a wearable device to map muscle signals to computer commands, helping retrain muscle activation patterns and reduce abnormal muscle coactivation after a stroke. This approach allows for high-dose, home-based training, which is not typically addressed by other therapies.
14589Eligibility Criteria
This trial is for individuals who have had a stroke, either recently (within the past 21 days) or chronically (at least 6 months ago). Participants must have severe motor impairment but some ability to move their shoulder and elbow. They cannot be part of another study, have significant cognitive or visual impairments, new spasticity treatments, anesthesia in the arm, or substantial pain that would prevent daily participation.Inclusion Criteria
I had a stroke over 6 months ago, have severe arm weakness but can still move my shoulder and elbow a bit.
I had my first stroke within the last 21 days and have severe arm weakness.
Exclusion Criteria
I have severe arm pain that stops me from participating in activities for 90 minutes a day.
I have a visual impairment that prevents me from seeing the whole screen.
You have trouble feeling or seeing on one side of your body due to a stroke.
+5 more
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Treatment
Participants use the myoelectric-computer interface (MCI) to improve arm movement by reducing abnormal muscle co-activation
6 weeks
Follow-up
Participants are monitored for safety and effectiveness after treatment
4 weeks
Participant Groups
The study is testing a wearable device called an MCI designed to improve arm movement by reducing muscle co-activation issues caused by strokes. It's a randomized controlled trial where participants will use the MCI at home and their progress with arm movements will be monitored.
6Treatment groups
Experimental Treatment
Placebo Group
Group I: Chronic stroke MCI while reachingExperimental Treatment1 Intervention
Decoupling muscles with MCI while reaching to targets
Group II: Chronic stroke MCI Electromyogram (EMG) pairsExperimental Treatment1 Intervention
Decoupling 2 muscles at a time with MCI
Group III: Chronic stroke MCI EMG tripletsExperimental Treatment1 Intervention
Decoupling 3 muscles at a time with MCI
Group IV: Acute stroke MCIExperimental Treatment1 Intervention
Decoupling muscles with MCI in acute stroke subjects
Group V: Chronic stroke Sham MCIPlacebo Group1 Intervention
Sham control group
Group VI: Acute stroke Sham MCIPlacebo Group1 Intervention
Acute stroke subjects sham comparator
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Northwestern UniversityChicago, IL
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Who Is Running the Clinical Trial?
Northwestern UniversityLead Sponsor
Shirley Ryan AbilityLabCollaborator
National Institutes of Health (NIH)Collaborator
References
Reducing Abnormal Muscle Coactivation After Stroke Using a Myoelectric-Computer Interface: A Pilot Study. [2021]Background A significant factor in impaired movement caused by stroke is the inability to activate muscles independently. Although the pathophysiology behind this abnormal coactivation is not clear, reducing the coactivation could improve overall arm function. A myoelectric computer interface (MCI), which maps electromyographic signals to cursor movement, could be used as a treatment to help retrain muscle activation patterns. Objective To investigate the use of MCI training to reduce abnormal muscle coactivation in chronic stroke survivors. Methods A total of 5 healthy participants and 5 stroke survivors with hemiparesis participated in multiple sessions of MCI training. The level of arm impairment in stroke survivors was assessed using the upper-extremity portion of the Fugl-Meyer Motor Assessment (FMA-UE). Participants performed isometric activations of up to 5 muscles. Activation of each muscle was mapped to different directions of cursor movement. The MCI specifically targeted 1 pair of muscles in each participant for reduction of coactivation. Results Both healthy participants and stroke survivors learned to reduce abnormal coactivation of the targeted muscles with MCI training. Out of 5 stroke survivors, 3 exhibited objective reduction in arm impairment as well (improvement in FMA-UE of 3 points in each of these patients). Conclusions These results suggest that the MCI was an effective tool in directly retraining muscle activation patterns following stroke.
The effect of myoelectric computer interface training on arm kinematics and function after stroke. [2020]Abnormal co-activation patterns of arm muscles is a substantial cause of impaired arm function after stroke. We designed a myoelectric computer interface (MCI) training paradigm to help stroke survivors reduce this abnormal coactivation. Here, we evaluated the effects of MCI training on function and arm kinematics in 32 chronic stroke survivors. We compared the effects of training duration and isometric vs. movement-based training conditions in 3 different groups. All groups reduced abnormal co-activation in targeted muscles, and showed reduced arm impairment after 6 weeks of training. They also showed improvements in arm kinematics as well as functional scores. Moreover, the gains persisted, though most were reduced, at one month after training stopped. These results suggest that MCI training holds promise to improve arm function after stroke.
Myoelectric computer interfaces to reduce co-contraction after stroke. [2021]A significant factor in impaired motor function caused by stroke is the inability to activate muscles independently. While the pathophysiology behind this co-contraction, sometimes called abnormal muscle synergy, is not clear, reducing the co-contraction could improve overall arm function. This pilot study describes the use of a myoelectric-computer interface (MCI) to retrain arm muscle activation and reduce co-contraction. We found that both healthy subjects and stroke survivors with hemiparesis learned to reduce co-contraction with MCI training. Three out of five stroke survivors experienced some improvement in arm function as well. These results suggest that MCIs could provide a novel, relatively inexpensive paradigm for stroke rehabilitation.
Wearable myoelectric interface enables high-dose, home-based training in severely impaired chronic stroke survivors. [2022]High-intensity occupational therapy can improve arm function after stroke, but many people lack access to such therapy. Home-based therapies could address this need, but they don't typically address abnormal muscle co-activation, an important aspect of arm impairment. An earlier study using lab-based, myoelectric computer interface game training enabled chronic stroke survivors to reduce abnormal co-activation and improve arm function. Here, we assess feasibility of doing this training at home using a novel, wearable, myoelectric interface for neurorehabilitation training (MINT) paradigm.
Brain-computer interfaces: Definitions and principles. [2021]Throughout life, the central nervous system (CNS) interacts with the world and with the body by activating muscles and excreting hormones. In contrast, brain-computer interfaces (BCIs) quantify CNS activity and translate it into new artificial outputs that replace, restore, enhance, supplement, or improve the natural CNS outputs. BCIs thereby modify the interactions between the CNS and the environment. Unlike the natural CNS outputs that come from spinal and brainstem motoneurons, BCI outputs come from brain signals that represent activity in other CNS areas, such as the sensorimotor cortex. If BCIs are to be useful for important communication and control tasks in real life, the CNS must control these brain signals nearly as reliably and accurately as it controls spinal motoneurons. To do this, they might, for example, need to incorporate software that mimics the function of the subcortical and spinal mechanisms that participate in normal movement control. The realization of high reliability and accuracy is perhaps the most difficult and critical challenge now facing BCI research and development. The ongoing adaptive modifications that maintain effective natural CNS outputs take place primarily in the CNS. The adaptive modifications that maintain effective BCI outputs can also take place in the BCI. This means that the BCI operation depends on the effective collaboration of two adaptive controllers, the CNS and the BCI. Realization of this second adaptive controller, the BCI, and management of its interactions with concurrent adaptations in the CNS comprise another complex and critical challenge for BCI development. BCIs can use different kinds of brain signals recorded in different ways from different brain areas. Decisions about which signals recorded in which ways from which brain areas should be selected for which applications are empirical questions that can only be properly answered by experiments. BCIs, like other communication and control technologies, often face artifacts that contaminate or imitate their chosen signals. Noninvasive BCIs (e.g., EEG- or fNIRS-based) need to take special care to avoid interpreting nonbrain signals (e.g., cranial EMG) as brain signals. This typically requires comprehensive topographical and spectral evaluations. In theory, the outputs of BCIs can select a goal or control a process. In the future, the most effective BCIs will probably be those that combine goal selection and process control so as to distribute control between the BCI and the application in a fashion suited to the current action. Through such distribution, BCIs may most effectively imitate natural CNS operation. The primary measure of BCI development is the extent to which BCI systems benefit people with neuromuscular disorders. Thus, BCI clinical evaluation, validation, and dissemination is a key step. It is at the same time a complex and difficult process that depends on multidisciplinary collaboration and management of the demanding requirements of clinical studies. Twenty-five years ago, BCI research was an esoteric endeavor pursued in only a few isolated laboratories. It is now a steadily growing field that engages many hundreds of scientists, engineers, and clinicians throughout the world in an increasingly interconnected community that is addressing the key issues and pursuing the high potential of BCI technology.
A Myoelectric Computer Interface for Reducing Abnormal Muscle Activations after Spinal Cord Injury. [2020]Myoelectric Computer Interfaces (MCIs) are a viable option to promote the recovery of movements following spinal cord injury (SCI), stroke, or other neurological disorders that impair motor functions. We developed and tested a MCI interface with the goal of reducing abnormal muscular activations due to compensatory strategies or undesired co-contraction after SCI. The interface mapped surface electromyographic signals (sEMG) into the movement of a cursor on a computer monitor. First, we aimed to reduce the co-activation of muscles pairs: the activation of two muscles controlled orthogonal directions of the cursor movements. Furthermore, to decrease the undesired concurrent activation of a third muscle, we modulated the visual feedback related to the position of the cursor on the screen based on the activation of this muscle. We tested the interface with six unimpaired and two SCI participants. Participants were able to decrease the activity of the targeted muscle when it was associated with the visual feedback of the cursor, but, interestingly, after training, its activity increased again. As for the SCI participants, one successfully decreased the co-activation of arm muscles, while the other successfully improved the selective activation of leg muscles. This is a first proof of concept that people with SCI can acquire, through the proposed MCI, a greater awareness of their muscular activity, reducing abnormal muscle simultaneous activations.
Myoelectric interface training enables targeted reduction in abnormal muscle co-activation. [2023]Abnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However, it is unclear to what extent the paradigm induced changes in the overall intermuscular coordination in the arm, as opposed to changing just the muscles trained with the MyoCI. This study examined the intermuscular coordination patterns of thirty-two stroke survivors who participated in 6 weeks of MyoCI training.
recoveriX: a new BCI-based technology for persons with stroke. [2020]Brain-computer interface (BCI) systems have been used primarily to provide communication for persons with severe movement disabilities. This paper presents a new system that extends BCI technology to a new patient group: persons diagnosed with stroke. This system, called recoveriX, is designed to detect changes in motor imagery in real-time to help monitor compliance and provide closed-loop feedback during therapy. We describe recoveriX and present initial results from one patient.
[Brain-Computer Interface: the First Clinical Experience in Russia]. [2018]Motor imagery is suggested to stimulate the same plastic mechanisms in the brain as a real movement. The brain-computer interface (BCI) controls motor imagery by converting EEG during this process into the commands for an external device. This article presents the results of two-stage study of the clinical use of non-invasive BCI in the rehabilitation of patients with severe hemiparesis caused by focal brain damage. It was found that the ability to control BCI did not depend on the duration of a disease, brain lesion localization and the degree of neurological deficit. The first step of the study involved 36 patients; it showed that the efficacy of rehabilitation was higher in the group with the use of BCI (the score on the Action Research Arm Test (ARAT) improved from 1 [0; 2] to 5 [0; 16] points, p = 0.012; no significant improvement was observed in control group). The second step of the study involved 19 patients; the complex BCI-exoskeleton (i.e. with the kinesthetic feedback) was used for motor imagery trainings. The improvement of the motor function of hands was proved by ARAT (the score improved from 2 [0; 37] to 4 [1; 45:5] points, p = 0.005) and Fugl-Meyer scale (from 72 [63; 110 ] to 79 [68; 115] points, p = 0.005).