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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/42574
Title: Enhancement of steady-state visual evoked potential-based brain–computer interface systems via a steady-state motion visual stimulus modality
Authors: Yunyong Punsawad
Yodchanan Wongsawat
Mahidol University
Keywords: Engineering
Issue Date: 1-Jun-2017
Citation: IEEJ Transactions on Electrical and Electronic Engineering. Vol.12, (2017), S89-S94
Abstract: © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems are among the most accurate assistive devices for patients with severe disabilities. However, existing visual stimulation patterns lead to eye fatigue, which affects the system performance. Therefore, in this study, we propose two improvements to SSVEP-based BCI systems. First, we propose a novel visual stimulator that incorporates a visual motion stimulus for the steady-state visual stimulus to reduce eye fatigue while maintaining the advantages associated with SSVEPs. We also propose two corresponding feature extraction algorithms, i.e. SSVEP detection and visual attention detection, to capture the phenomena of steady-state motion visual stimulus responses. The accuracy of the test was ∼83.6%. Second, we propose a novel hybrid BCI-SSVEP system and a motion visual stimulus hybrid BCI system to enhance the SSVEP-based BCI system during a state of eye fatigue. Participants used the SSVEP system until reaching a fatigued state and then began using a hybrid motion visual stimulus. The accuracy of the proposed system was ∼85.6%. The proposed improvements can be incorporated into practical BCI systems for wheelchair control. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85020247685&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/42574
ISSN: 19314981
19314973
Appears in Collections:Scopus 2016-2017

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