Cargando…

Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions

BACKGROUND: As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to i...

Descripción completa

Detalles Bibliográficos
Autores principales: Hortal, Enrique, Planelles, Daniel, Resquin, Francisco, Climent, José M., Azorín, José M., Pons, José L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4609472/
https://www.ncbi.nlm.nih.gov/pubmed/26476869
http://dx.doi.org/10.1186/s12984-015-0082-9
_version_ 1782395819601690624
author Hortal, Enrique
Planelles, Daniel
Resquin, Francisco
Climent, José M.
Azorín, José M.
Pons, José L.
author_facet Hortal, Enrique
Planelles, Daniel
Resquin, Francisco
Climent, José M.
Azorín, José M.
Pons, José L.
author_sort Hortal, Enrique
collection PubMed
description BACKGROUND: As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes. METHODS: In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection. RESULTS: Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively). CONCLUSIONS: The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.
format Online
Article
Text
id pubmed-4609472
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-46094722015-10-19 Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions Hortal, Enrique Planelles, Daniel Resquin, Francisco Climent, José M. Azorín, José M. Pons, José L. J Neuroeng Rehabil Research BACKGROUND: As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes. METHODS: In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection. RESULTS: Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively). CONCLUSIONS: The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure. BioMed Central 2015-10-17 /pmc/articles/PMC4609472/ /pubmed/26476869 http://dx.doi.org/10.1186/s12984-015-0082-9 Text en © Hortal et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Hortal, Enrique
Planelles, Daniel
Resquin, Francisco
Climent, José M.
Azorín, José M.
Pons, José L.
Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions
title Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions
title_full Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions
title_fullStr Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions
title_full_unstemmed Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions
title_short Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions
title_sort using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4609472/
https://www.ncbi.nlm.nih.gov/pubmed/26476869
http://dx.doi.org/10.1186/s12984-015-0082-9
work_keys_str_mv AT hortalenrique usingabrainmachineinterfacetocontrolahybridupperlimbexoskeletonduringrehabilitationofpatientswithneurologicalconditions
AT planellesdaniel usingabrainmachineinterfacetocontrolahybridupperlimbexoskeletonduringrehabilitationofpatientswithneurologicalconditions
AT resquinfrancisco usingabrainmachineinterfacetocontrolahybridupperlimbexoskeletonduringrehabilitationofpatientswithneurologicalconditions
AT climentjosem usingabrainmachineinterfacetocontrolahybridupperlimbexoskeletonduringrehabilitationofpatientswithneurologicalconditions
AT azorinjosem usingabrainmachineinterfacetocontrolahybridupperlimbexoskeletonduringrehabilitationofpatientswithneurologicalconditions
AT ponsjosel usingabrainmachineinterfacetocontrolahybridupperlimbexoskeletonduringrehabilitationofpatientswithneurologicalconditions