Cargando…

First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier

A new approach in motor rehabilitation after stroke is to use motor imagery (MI). To give feedback on MI performance brain–computer interface (BCIs) can be used. This requires a fast and easy acquisition of a reliable classifier. Usually, for training a classifier, electroencephalogram (EEG) data of...

Descripción completa

Detalles Bibliográficos
Autores principales: Kaiser, Vera, Kreilinger, Alex, Müller-Putz, Gernot R., Neuper, Christa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3132635/
https://www.ncbi.nlm.nih.gov/pubmed/21779234
http://dx.doi.org/10.3389/fnins.2011.00086
_version_ 1782207848755757056
author Kaiser, Vera
Kreilinger, Alex
Müller-Putz, Gernot R.
Neuper, Christa
author_facet Kaiser, Vera
Kreilinger, Alex
Müller-Putz, Gernot R.
Neuper, Christa
author_sort Kaiser, Vera
collection PubMed
description A new approach in motor rehabilitation after stroke is to use motor imagery (MI). To give feedback on MI performance brain–computer interface (BCIs) can be used. This requires a fast and easy acquisition of a reliable classifier. Usually, for training a classifier, electroencephalogram (EEG) data of MI without feedback is used, but it would be advantageous if we could give feedback right from the beginning. The sensorimotor EEG changes of the motor cortex during active and passive movement (PM) and MI are similar. The aim of this study is to explore, whether it is possible to use EEG data from active or PM to set up a classifier for the detection of MI in a group of elderly persons. In addition, the activation patterns of the motor cortical areas of elderly persons were analyzed during different motor tasks. EEG was recorded from three Laplacian channels over the sensorimotor cortex in a sample of 19 healthy elderly volunteers. Participants performed three different tasks in consecutive order, passive, active hand movement, and hand MI. Classifiers were calculated with data of every task. These classifiers were then used to detect event-related desynchronization (ERD) in the MI data. ERD values, related to the different tasks, were calculated and analyzed statistically. The performance of classifiers calculated from passive and active hand movement data did not differ significantly regarding the classification accuracy for detecting MI. The EEG patterns of the motor cortical areas during the different tasks was similar to the patterns normally found in younger persons but more widespread regarding localization and frequency range of the ERD. In this study, we have shown that it is possible to use classifiers calculated with data from passive and active hand movement to detect MI. Hence, for working with stroke patients, a physiotherapy session could be used to obtain data for classifier set up and the BCI-rehabilitation training could start immediately.
format Online
Article
Text
id pubmed-3132635
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher Frontiers Research Foundation
record_format MEDLINE/PubMed
spelling pubmed-31326352011-07-21 First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier Kaiser, Vera Kreilinger, Alex Müller-Putz, Gernot R. Neuper, Christa Front Neurosci Neuroscience A new approach in motor rehabilitation after stroke is to use motor imagery (MI). To give feedback on MI performance brain–computer interface (BCIs) can be used. This requires a fast and easy acquisition of a reliable classifier. Usually, for training a classifier, electroencephalogram (EEG) data of MI without feedback is used, but it would be advantageous if we could give feedback right from the beginning. The sensorimotor EEG changes of the motor cortex during active and passive movement (PM) and MI are similar. The aim of this study is to explore, whether it is possible to use EEG data from active or PM to set up a classifier for the detection of MI in a group of elderly persons. In addition, the activation patterns of the motor cortical areas of elderly persons were analyzed during different motor tasks. EEG was recorded from three Laplacian channels over the sensorimotor cortex in a sample of 19 healthy elderly volunteers. Participants performed three different tasks in consecutive order, passive, active hand movement, and hand MI. Classifiers were calculated with data of every task. These classifiers were then used to detect event-related desynchronization (ERD) in the MI data. ERD values, related to the different tasks, were calculated and analyzed statistically. The performance of classifiers calculated from passive and active hand movement data did not differ significantly regarding the classification accuracy for detecting MI. The EEG patterns of the motor cortical areas during the different tasks was similar to the patterns normally found in younger persons but more widespread regarding localization and frequency range of the ERD. In this study, we have shown that it is possible to use classifiers calculated with data from passive and active hand movement to detect MI. Hence, for working with stroke patients, a physiotherapy session could be used to obtain data for classifier set up and the BCI-rehabilitation training could start immediately. Frontiers Research Foundation 2011-07-05 /pmc/articles/PMC3132635/ /pubmed/21779234 http://dx.doi.org/10.3389/fnins.2011.00086 Text en Copyright © 2011 Kaiser, Kreilinger, Müller-Putz and Neuper. http://www.frontiersin.org/licenseagreement This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.
spellingShingle Neuroscience
Kaiser, Vera
Kreilinger, Alex
Müller-Putz, Gernot R.
Neuper, Christa
First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier
title First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier
title_full First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier
title_fullStr First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier
title_full_unstemmed First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier
title_short First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier
title_sort first steps toward a motor imagery based stroke bci: new strategy to set up a classifier
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3132635/
https://www.ncbi.nlm.nih.gov/pubmed/21779234
http://dx.doi.org/10.3389/fnins.2011.00086
work_keys_str_mv AT kaiservera firststepstowardamotorimagerybasedstrokebcinewstrategytosetupaclassifier
AT kreilingeralex firststepstowardamotorimagerybasedstrokebcinewstrategytosetupaclassifier
AT mullerputzgernotr firststepstowardamotorimagerybasedstrokebcinewstrategytosetupaclassifier
AT neuperchrista firststepstowardamotorimagerybasedstrokebcinewstrategytosetupaclassifier