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Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling

The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired th...

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Autores principales: Tang, Jiabei, Xu, Minpeng, Han, Jin, Liu, Miao, Dai, Tingfei, Chen, Shanguang, Ming, Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435370/
https://www.ncbi.nlm.nih.gov/pubmed/32731432
http://dx.doi.org/10.3390/s20154186
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author Tang, Jiabei
Xu, Minpeng
Han, Jin
Liu, Miao
Dai, Tingfei
Chen, Shanguang
Ming, Dong
author_facet Tang, Jiabei
Xu, Minpeng
Han, Jin
Liu, Miao
Dai, Tingfei
Chen, Shanguang
Ming, Dong
author_sort Tang, Jiabei
collection PubMed
description The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.
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spelling pubmed-74353702020-08-28 Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling Tang, Jiabei Xu, Minpeng Han, Jin Liu, Miao Dai, Tingfei Chen, Shanguang Ming, Dong Sensors (Basel) Article The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling. MDPI 2020-07-28 /pmc/articles/PMC7435370/ /pubmed/32731432 http://dx.doi.org/10.3390/s20154186 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tang, Jiabei
Xu, Minpeng
Han, Jin
Liu, Miao
Dai, Tingfei
Chen, Shanguang
Ming, Dong
Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling
title Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling
title_full Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling
title_fullStr Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling
title_full_unstemmed Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling
title_short Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling
title_sort optimizing ssvep-based bci system towards practical high-speed spelling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435370/
https://www.ncbi.nlm.nih.gov/pubmed/32731432
http://dx.doi.org/10.3390/s20154186
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