Cargando…
Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP
Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction algorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated various advantages of hybrid BCI systems in terms of an...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5564129/ https://www.ncbi.nlm.nih.gov/pubmed/29065590 http://dx.doi.org/10.1155/2017/3789386 |
_version_ | 1783258211645652992 |
---|---|
author | Ko, Li-Wei Ranga, S. S. K. Komarov, Oleksii Chen, Chung-Chiang |
author_facet | Ko, Li-Wei Ranga, S. S. K. Komarov, Oleksii Chen, Chung-Chiang |
author_sort | Ko, Li-Wei |
collection | PubMed |
description | Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction algorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated various advantages of hybrid BCI systems in terms of an improved accuracy or number of commands available for the user. But still, BCI systems are far from realization for daily use. Having high performance with less number of channels is one of the challenging issues that persists, especially with hybrid BCI systems, where multiple channels are necessary to record information from two or more EEG signal components. Therefore, this work proposes a single-channel (C3 or C4) hybrid BCI system that combines motor imagery (MI) and steady-state visually evoked potential (SSVEP) approaches. This study demonstrates that besides MI features, SSVEP features can also be captured from C3 or C4 channel. The results show that due to rich feature information (MI and SSVEP) at these channels, the proposed hybrid BCI system outperforms both MI- and SSVEP-based systems having an average classification accuracy of 85.6 ± 7.7% in a two-class task. |
format | Online Article Text |
id | pubmed-5564129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-55641292017-08-28 Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP Ko, Li-Wei Ranga, S. S. K. Komarov, Oleksii Chen, Chung-Chiang J Healthc Eng Research Article Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction algorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated various advantages of hybrid BCI systems in terms of an improved accuracy or number of commands available for the user. But still, BCI systems are far from realization for daily use. Having high performance with less number of channels is one of the challenging issues that persists, especially with hybrid BCI systems, where multiple channels are necessary to record information from two or more EEG signal components. Therefore, this work proposes a single-channel (C3 or C4) hybrid BCI system that combines motor imagery (MI) and steady-state visually evoked potential (SSVEP) approaches. This study demonstrates that besides MI features, SSVEP features can also be captured from C3 or C4 channel. The results show that due to rich feature information (MI and SSVEP) at these channels, the proposed hybrid BCI system outperforms both MI- and SSVEP-based systems having an average classification accuracy of 85.6 ± 7.7% in a two-class task. Hindawi 2017 2017-08-07 /pmc/articles/PMC5564129/ /pubmed/29065590 http://dx.doi.org/10.1155/2017/3789386 Text en Copyright © 2017 Li-Wei Ko et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ko, Li-Wei Ranga, S. S. K. Komarov, Oleksii Chen, Chung-Chiang Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP |
title | Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP |
title_full | Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP |
title_fullStr | Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP |
title_full_unstemmed | Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP |
title_short | Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP |
title_sort | development of single-channel hybrid bci system using motor imagery and ssvep |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5564129/ https://www.ncbi.nlm.nih.gov/pubmed/29065590 http://dx.doi.org/10.1155/2017/3789386 |
work_keys_str_mv | AT koliwei developmentofsinglechannelhybridbcisystemusingmotorimageryandssvep AT rangassk developmentofsinglechannelhybridbcisystemusingmotorimageryandssvep AT komarovoleksii developmentofsinglechannelhybridbcisystemusingmotorimageryandssvep AT chenchungchiang developmentofsinglechannelhybridbcisystemusingmotorimageryandssvep |