Evidence map›Paper›PMID 32635550›Full record

ArticleSensors (Basel, Switzerland)2020

A Virtual Reality Muscle-Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study.

Octavio Marin-Pardo, Christopher M Laine, Miranda Rennie, Kaori L Ito, James Finley, Sook-Lei Liew

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.6field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 35 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
  4. Virtual Reality-Based Rehabilitation Therapy.Physiological research · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Functional and neuromuscular changes inducedFrontiers in neuroergonomics · 2022
    Article
  14. Article
  15. Conducting a VR Clinical Trial in the Era of COVID-19.Frontiers in virtual reality · 2021
    Article
  16. Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Octavio Marin-PardoDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.ORCID 0000-0002-8923-254X
Christopher M LaineChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA.
Miranda RennieChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA.
Kaori L ItoChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA.
James FinleyDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.ORCID 0000-0003-2679-2221
Sook-Lei LiewDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.ORCID 0000-0001-5935-4215
University of Southern California · US

Funding

Big Data Neuroimaging to Predict Motor Behavior After StrokeK01HD091283 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LIEW, SOOK-LEI · 2017 to 2020
$802k
American Heart Association 16IRG26960017Army Research Office W911NNF-14-D-0005NIH HHS K01HD091283
6 · The paper itself

Abstract

Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain-computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain-computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12-30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle-computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.

Indexed as

Neurological RehabilitationStroke RehabilitationUser-Computer InterfaceVirtual RealityAdultAgedComputersFemaleHumansMaleMiddle AgedPilot ProjectsRecovery of FunctionStrokeTreatment Outcomebiofeedbackbrain–computer interfaceco-contractioncorticomuscular coherenceelectromyographyneurorehabilitationstrokevirtual reality

Identifiers

PMID32635550
PMCPMC7374440
OpenAlexW3040004003

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.