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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/42582
Title: Multi-command SSAEP-based BCI system with training sessions for SSVEP during an eye fatigue state
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), S72-S78
Abstract: © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. This paper proposes a steady-state auditory stimulus modality and a detection algorithm to replace steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems during visual fatigue periods. The optimal speaker position for the steady-state auditory evoked potential (SSAEP)-based BCI system and possible electrode positions are investigated. Using the proposed system, an accuracy of 85% for two commands was achieved based on the T3–T5 and T4–T6 electrode positions using only one speaker. SSAEP is a promising BCI modality for mitigating the problem of eye fatigue that often occurs during the use of SSVEP-based BCI systems. However, SSAEP-based BCI systems suffer from low accuracy. To increase accuracy, we propose a new enhanced SSAEP training method. The training process was enhanced by instructing users to control their attention levels while simultaneously detecting an auditory stimulus frequency. Furthermore, we propose a corresponding single-frequency, multi-command BCI paradigm for the proposed training method. With the proposed paradigm, four commands can be detected using only one auditory stimulus frequency. The proposed training system yielded an accuracy of ∼81% compared to 66% for sessions performed without the proposed training method. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85020252830&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/42582
ISSN: 19314981
19314973
Appears in Collections:Scopus 2016-2017

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