Li, X. orcid.org/0000-0002-2960-9608, Wang, J. orcid.org/0009-0002-3715-1980
, Cao, X. et al. (5 more authors)
(2024)
Evaluation of an online SSVEP-BCI with fast system setup.
Journal of Neurorestoratology, 12 (2).
100122.
ISSN 2324-2426
Abstract
The brain–computer interface (BCI) plays an important role in neural restoration. Current BCI systems generally require complex experimental preparation to perform well, but this time-consuming process may hinder their use in clinical applications. To explore the feasibility of simplifying the BCI system setup, a wearable BCI system based on the steady-state visual evoked potential (SSVEP) was developed and evaluated. Fifteen healthy participants were recruited to test the fast-setup system using dry and wet electrodes in a real-life scenario. In this study, the average system setup time for the dry electrode was 38.40 seconds and that for the wet electrode was 103.40 seconds, which are times appreciably shorter than those in previous BCI experiments, enabling a rapid setup of the BCI system. Although the electroencephalogram (EEG) signal quality was low in this fast-setup BCI experiment, the BCI system achieved an information transfer rate of 138.89 bits/min with an eight-channel wet electrode and an information transfer rate of 70.59 bits/min with an eight-channel dry electrode, showing that the overall performance was close to that in traditional experiments. In addition, the results suggest that the solutions of a multi-channel dry electrode or few-channel wet electrode may be suitable for the fast-setup SSEVP-BCI. This fast-setup SSVEP-BCI has the advantages of simple preparation and stable performance and is thus conducive to promoting the use of the BCI in clinical practice.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY-NC-ND 4.0). |
Keywords: | Brain–computer interface, Steady-state visual evoked potential, System setup, Online adaptive canonical correlation analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number EU - European Union EP/Y037367/1 UKRI (UK Research and Innovation) Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 May 2024 09:17 |
Last Modified: | 23 May 2024 09:17 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jnrt.2024.100122 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212710 |
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