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...

Full description

Bibliographic Details
Main Authors:	Kaiser, Vera, Kreilinger, Alex, Müller-Putz, Gernot R., Neuper, Christa
Format:	Online
Language:	English
Published:	Frontiers Research Foundation 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3132635/

id	pubmed-3132635
recordtype	oai_dc
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 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Kaiser, Vera Kreilinger, Alex Müller-Putz, Gernot R. Neuper, Christa
spellingShingle	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
author_facet	Kaiser, Vera Kreilinger, Alex Müller-Putz, Gernot R. Neuper, Christa
author_sort	Kaiser, Vera
title	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_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_sort	first steps toward a motor imagery based stroke bci: new strategy to set up a classifier
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.
publisher	Frontiers Research Foundation
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3132635/
_version_	1611464579873767424

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

Similar Items