A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study
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 di...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-07-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/13/3754 |
id |
doaj-e2994b8285ab465ea65026aba8a201c7 |
---|---|
record_format |
Article |
spelling |
doaj-e2994b8285ab465ea65026aba8a201c72020-11-25T02:32:20ZengMDPI AGSensors1424-82202020-07-01203754375410.3390/s20133754A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot StudyOctavio Marin-Pardo0Christopher M. Laine1Miranda Rennie2Kaori L. Ito3James Finley4Sook-Lei Liew5Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USAChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USAChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USAChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USADepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USADepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USASevere 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.https://www.mdpi.com/1424-8220/20/13/3754biofeedbackstrokebrain–computer interfaceneurorehabilitationcorticomuscular coherenceelectromyography |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Octavio Marin-Pardo Christopher M. Laine Miranda Rennie Kaori L. Ito James Finley Sook-Lei Liew |
spellingShingle |
Octavio Marin-Pardo Christopher M. Laine Miranda Rennie Kaori L. Ito James Finley Sook-Lei Liew A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study Sensors biofeedback stroke brain–computer interface neurorehabilitation corticomuscular coherence electromyography |
author_facet |
Octavio Marin-Pardo Christopher M. Laine Miranda Rennie Kaori L. Ito James Finley Sook-Lei Liew |
author_sort |
Octavio Marin-Pardo |
title |
A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study |
title_short |
A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study |
title_full |
A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study |
title_fullStr |
A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study |
title_full_unstemmed |
A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study |
title_sort |
virtual reality muscle–computer interface for neurorehabilitation in chronic stroke: a pilot study |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-07-01 |
description |
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. |
topic |
biofeedback stroke brain–computer interface neurorehabilitation corticomuscular coherence electromyography |
url |
https://www.mdpi.com/1424-8220/20/13/3754 |
work_keys_str_mv |
AT octaviomarinpardo avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT christophermlaine avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT mirandarennie avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT kaorilito avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT jamesfinley avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT sookleiliew avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT octaviomarinpardo virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT christophermlaine virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT mirandarennie virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT kaorilito virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT jamesfinley virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy AT sookleiliew virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy |
_version_ |
1724819902912528384 |