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A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes

A high-speed steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system using dry EEG electrodes was demonstrated in this study. The dry electrode was fabricated in our laboratory. It was designed as claw-like structure with a diameter of 14 mm, featuring 8 small finge...

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Autores principales: Xing, Xiao, Wang, Yijun, Pei, Weihua, Guo, Xuhong, Liu, Zhiduo, Wang, Fei, Ming, Gege, Zhao, Hongze, Gui, Qiang, Chen, Hongda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168577/
https://www.ncbi.nlm.nih.gov/pubmed/30279463
http://dx.doi.org/10.1038/s41598-018-32283-8
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author Xing, Xiao
Wang, Yijun
Pei, Weihua
Guo, Xuhong
Liu, Zhiduo
Wang, Fei
Ming, Gege
Zhao, Hongze
Gui, Qiang
Chen, Hongda
author_facet Xing, Xiao
Wang, Yijun
Pei, Weihua
Guo, Xuhong
Liu, Zhiduo
Wang, Fei
Ming, Gege
Zhao, Hongze
Gui, Qiang
Chen, Hongda
author_sort Xing, Xiao
collection PubMed
description A high-speed steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system using dry EEG electrodes was demonstrated in this study. The dry electrode was fabricated in our laboratory. It was designed as claw-like structure with a diameter of 14 mm, featuring 8 small fingers of 6 mm length and 2 mm diameter. The structure and elasticity can help the fingers pass through the hair and contact the scalp when the electrode is placed on head. The electrode was capable of recording spontaneous EEG and evoked brain activities such as SSVEP with high signal-to-noise ratio. This study implemented a twelve-class SSVEP-based BCI system with eight electrodes embedded in a headband. Subjects also completed a comfort level questionnaire with the dry electrodes. Using a preprocessing algorithm of filter bank analysis (FBA) and a classification algorithm based on task-related component analysis (TRCA), the average classification accuracy of eleven participants was 93.2% using 1-second-long SSVEPs, leading to an average information transfer rate (ITR) of 92.35 bits/min. All subjects did not report obvious discomfort with the dry electrodes. This result represented the highest communication speed in the dry-electrode based BCI systems. The proposed system could provide a comfortable user experience and a stable control method for developing practical BCIs.
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spelling pubmed-61685772018-10-05 A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes Xing, Xiao Wang, Yijun Pei, Weihua Guo, Xuhong Liu, Zhiduo Wang, Fei Ming, Gege Zhao, Hongze Gui, Qiang Chen, Hongda Sci Rep Article A high-speed steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system using dry EEG electrodes was demonstrated in this study. The dry electrode was fabricated in our laboratory. It was designed as claw-like structure with a diameter of 14 mm, featuring 8 small fingers of 6 mm length and 2 mm diameter. The structure and elasticity can help the fingers pass through the hair and contact the scalp when the electrode is placed on head. The electrode was capable of recording spontaneous EEG and evoked brain activities such as SSVEP with high signal-to-noise ratio. This study implemented a twelve-class SSVEP-based BCI system with eight electrodes embedded in a headband. Subjects also completed a comfort level questionnaire with the dry electrodes. Using a preprocessing algorithm of filter bank analysis (FBA) and a classification algorithm based on task-related component analysis (TRCA), the average classification accuracy of eleven participants was 93.2% using 1-second-long SSVEPs, leading to an average information transfer rate (ITR) of 92.35 bits/min. All subjects did not report obvious discomfort with the dry electrodes. This result represented the highest communication speed in the dry-electrode based BCI systems. The proposed system could provide a comfortable user experience and a stable control method for developing practical BCIs. Nature Publishing Group UK 2018-10-02 /pmc/articles/PMC6168577/ /pubmed/30279463 http://dx.doi.org/10.1038/s41598-018-32283-8 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Xing, Xiao
Wang, Yijun
Pei, Weihua
Guo, Xuhong
Liu, Zhiduo
Wang, Fei
Ming, Gege
Zhao, Hongze
Gui, Qiang
Chen, Hongda
A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes
title A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes
title_full A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes
title_fullStr A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes
title_full_unstemmed A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes
title_short A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes
title_sort high-speed ssvep-based bci using dry eeg electrodes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168577/
https://www.ncbi.nlm.nih.gov/pubmed/30279463
http://dx.doi.org/10.1038/s41598-018-32283-8
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