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A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study
BACKGROUND: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5594542/ https://www.ncbi.nlm.nih.gov/pubmed/28893295 http://dx.doi.org/10.1186/s12984-017-0307-1 |
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author | Wang, Kun Wang, Zhongpeng Guo, Yi He, Feng Qi, Hongzhi Xu, Minpeng Ming, Dong |
author_facet | Wang, Kun Wang, Zhongpeng Guo, Yi He, Feng Qi, Hongzhi Xu, Minpeng Ming, Dong |
author_sort | Wang, Kun |
collection | PubMed |
description | BACKGROUND: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads. METHODS: Eleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks. RESULTS: All subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution. CONCLUSIONS: This paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities. |
format | Online Article Text |
id | pubmed-5594542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55945422017-09-14 A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study Wang, Kun Wang, Zhongpeng Guo, Yi He, Feng Qi, Hongzhi Xu, Minpeng Ming, Dong J Neuroeng Rehabil Research BACKGROUND: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads. METHODS: Eleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks. RESULTS: All subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution. CONCLUSIONS: This paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities. BioMed Central 2017-09-11 /pmc/articles/PMC5594542/ /pubmed/28893295 http://dx.doi.org/10.1186/s12984-017-0307-1 Text en © The Author(s). 2017 Open AccessThis 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 Wang, Kun Wang, Zhongpeng Guo, Yi He, Feng Qi, Hongzhi Xu, Minpeng Ming, Dong A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study |
title | A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study |
title_full | A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study |
title_fullStr | A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study |
title_full_unstemmed | A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study |
title_short | A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study |
title_sort | brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5594542/ https://www.ncbi.nlm.nih.gov/pubmed/28893295 http://dx.doi.org/10.1186/s12984-017-0307-1 |
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