Visual stimulus background effects on SSVEP-Based BCI towards a practical Robot car control

Xiaokang Shu; Lin Yao; Jianjun Meng; Xinjun Sheng; Xiangyang Zhu

doi:10.1142/S0219843615500140

Visual stimulus background effects on SSVEP-Based BCI towards a practical Robot car control

Xiaokang Shu, Lin Yao, Jianjun Meng, Xinjun Sheng, Xiangyang Zhu

Biomedical Engineering

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Flickering source is an indispensable component in steady-state visual evoked potentials (SSVEPs)-based brain-computer interface (BCI), and its background severely influences the potentials evoked by the repetitive stimuli. In this paper, we investigated the problem under three different backgrounds in the context of the SSVEP-BCI-based robot car control, including black screen, static scene and dynamic scene of the environment. In the ten subjects experiment, we found significant decrease in SSVEP amplitude in dynamic scene condition compared to the reference condition black screen (p < 0.05), which resulted in classification accuracy decrease as evaluated by 10-fold cross validation. However, our proposed experiment paradigm has shown that training with static scene or dynamic scene condition could well compensate this performance drop and improve the online robot car control with real-time video feedback. The addressed problem in our application would provide some valuable suggestions when translating the SSVEP-BCI from laboratory exploration into practical usages.

Original language	English (US)
Article number	1550014
Journal	International Journal of Humanoid Robotics
Volume	12
Issue number	2
DOIs	https://doi.org/10.1142/S0219843615500140
State	Published - Jun 29 2015

Bibliographical note

Funding Information:
We thank all volunteers for their participation in the study. This paper is supported by the National Basic Research Program (973 Program) of China (Grant No. 2011CB013305), the Science and Technology Commission of Shanghai Municipality (Grant No. 13430721600), and the National Natural Science Foundation of China (Grant Nos. 51375296, 51421092).

Keywords

BCI
SSVEP
Stimulation background
robot car control

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title = "Visual stimulus background effects on SSVEP-Based BCI towards a practical Robot car control",

abstract = "Flickering source is an indispensable component in steady-state visual evoked potentials (SSVEPs)-based brain-computer interface (BCI), and its background severely influences the potentials evoked by the repetitive stimuli. In this paper, we investigated the problem under three different backgrounds in the context of the SSVEP-BCI-based robot car control, including black screen, static scene and dynamic scene of the environment. In the ten subjects experiment, we found significant decrease in SSVEP amplitude in dynamic scene condition compared to the reference condition black screen (p < 0.05), which resulted in classification accuracy decrease as evaluated by 10-fold cross validation. However, our proposed experiment paradigm has shown that training with static scene or dynamic scene condition could well compensate this performance drop and improve the online robot car control with real-time video feedback. The addressed problem in our application would provide some valuable suggestions when translating the SSVEP-BCI from laboratory exploration into practical usages.",

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author = "Xiaokang Shu and Lin Yao and Jianjun Meng and Xinjun Sheng and Xiangyang Zhu",

note = "Funding Information: We thank all volunteers for their participation in the study. This paper is supported by the National Basic Research Program (973 Program) of China (Grant No. 2011CB013305), the Science and Technology Commission of Shanghai Municipality (Grant No. 13430721600), and the National Natural Science Foundation of China (Grant Nos. 51375296, 51421092).",

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AU - Zhu, Xiangyang

N1 - Funding Information: We thank all volunteers for their participation in the study. This paper is supported by the National Basic Research Program (973 Program) of China (Grant No. 2011CB013305), the Science and Technology Commission of Shanghai Municipality (Grant No. 13430721600), and the National Natural Science Foundation of China (Grant Nos. 51375296, 51421092).

PY - 2015/6/29

Y1 - 2015/6/29

N2 - Flickering source is an indispensable component in steady-state visual evoked potentials (SSVEPs)-based brain-computer interface (BCI), and its background severely influences the potentials evoked by the repetitive stimuli. In this paper, we investigated the problem under three different backgrounds in the context of the SSVEP-BCI-based robot car control, including black screen, static scene and dynamic scene of the environment. In the ten subjects experiment, we found significant decrease in SSVEP amplitude in dynamic scene condition compared to the reference condition black screen (p < 0.05), which resulted in classification accuracy decrease as evaluated by 10-fold cross validation. However, our proposed experiment paradigm has shown that training with static scene or dynamic scene condition could well compensate this performance drop and improve the online robot car control with real-time video feedback. The addressed problem in our application would provide some valuable suggestions when translating the SSVEP-BCI from laboratory exploration into practical usages.

AB - Flickering source is an indispensable component in steady-state visual evoked potentials (SSVEPs)-based brain-computer interface (BCI), and its background severely influences the potentials evoked by the repetitive stimuli. In this paper, we investigated the problem under three different backgrounds in the context of the SSVEP-BCI-based robot car control, including black screen, static scene and dynamic scene of the environment. In the ten subjects experiment, we found significant decrease in SSVEP amplitude in dynamic scene condition compared to the reference condition black screen (p < 0.05), which resulted in classification accuracy decrease as evaluated by 10-fold cross validation. However, our proposed experiment paradigm has shown that training with static scene or dynamic scene condition could well compensate this performance drop and improve the online robot car control with real-time video feedback. The addressed problem in our application would provide some valuable suggestions when translating the SSVEP-BCI from laboratory exploration into practical usages.

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Visual stimulus background effects on SSVEP-Based BCI towards a practical Robot car control

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