Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Marin-Pardo, Octavio, Laine, Christopher M., Rennie, Miranda, Ito, Kaori L., Finley, James, Liew, Sook-Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374440/
https://www.ncbi.nlm.nih.gov/pubmed/32635550
http://dx.doi.org/10.3390/s20133754
_version_ 1783561699623698432
author Marin-Pardo, Octavio
Laine, Christopher M.
Rennie, Miranda
Ito, Kaori L.
Finley, James
Liew, Sook-Lei
author_facet Marin-Pardo, Octavio
Laine, Christopher M.
Rennie, Miranda
Ito, Kaori L.
Finley, James
Liew, Sook-Lei
author_sort Marin-Pardo, Octavio
collection PubMed
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.
format Online
Article
Text
id pubmed-7374440
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-73744402020-08-06 A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study Marin-Pardo, Octavio Laine, Christopher M. Rennie, Miranda Ito, Kaori L. Finley, James Liew, Sook-Lei Sensors (Basel) Article 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. MDPI 2020-07-04 /pmc/articles/PMC7374440/ /pubmed/32635550 http://dx.doi.org/10.3390/s20133754 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marin-Pardo, Octavio
Laine, Christopher M.
Rennie, Miranda
Ito, Kaori L.
Finley, James
Liew, Sook-Lei
A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374440/
https://www.ncbi.nlm.nih.gov/pubmed/32635550
http://dx.doi.org/10.3390/s20133754
work_keys_str_mv AT marinpardooctavio avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT lainechristopherm avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT renniemiranda avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT itokaoril avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT finleyjames avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT liewsooklei avirtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT marinpardooctavio virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT lainechristopherm virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT renniemiranda virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT itokaoril virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT finleyjames virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy
AT liewsooklei virtualrealitymusclecomputerinterfaceforneurorehabilitationinchronicstrokeapilotstudy