Smart home has been widely used to improve the living quality of people. Recently, the brain-computer interface (BCI) contributes greatly to the smart home system. We design a BCI-based smart home system, in which the event-related potentials (ERP) are induced by the image interface based on the oddball paradigm. Then, we investigate the influence of mental fatigue on the ERP classification by the Fisher linear discriminant analysis. The results indicate that the classification accuracy of ERP decreases as the brain evolves from the normal stage to the mental fatigue stage. In order to probe into the difference of the brain, cognitive process between mental fatigue and normal states, we construct multivariate weighted recurrence networks and analyze the variation of the weighted clustering coefficient and weighted global efficiency corresponding to these two brain states. The findings suggest that these two network metrics allow distinguishing normal and mental fatigue states and yield novel insights into the brain fatigue behavior resulting from a long use of the ERP-based smart home system. These properties render the multivariate recurrence network, particularly useful for analyzing electroencephalographic recordings from the ERP-based smart home system.
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August 2018
Research Article|
August 27 2018
Multivariate weighted recurrence network analysis of EEG signals from ERP-based smart home system
Special Collection:
Recurrence Quantification Analysis for Understanding Complex Systems
Zhong-Ke Gao;
Zhong-Ke Gao
a)
School of Electrical and Information Engineering, Tianjin University
, Tianjin 300072, China
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Cheng-Yong Liu;
Cheng-Yong Liu
School of Electrical and Information Engineering, Tianjin University
, Tianjin 300072, China
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Yu-Xuan Yang;
Yu-Xuan Yang
School of Electrical and Information Engineering, Tianjin University
, Tianjin 300072, China
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Qing Cai;
Qing Cai
School of Electrical and Information Engineering, Tianjin University
, Tianjin 300072, China
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Wei-Dong Dang;
Wei-Dong Dang
School of Electrical and Information Engineering, Tianjin University
, Tianjin 300072, China
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Xiu-Lan Du;
Xiu-Lan Du
School of Electrical and Information Engineering, Tianjin University
, Tianjin 300072, China
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Hao-Xuan Jia
Hao-Xuan Jia
School of Electrical and Information Engineering, Tianjin University
, Tianjin 300072, China
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a)
Electronic mail: [email protected]
Chaos 28, 085713 (2018)
Article history
Received:
December 10 2017
Accepted:
April 09 2018
Citation
Zhong-Ke Gao, Cheng-Yong Liu, Yu-Xuan Yang, Qing Cai, Wei-Dong Dang, Xiu-Lan Du, Hao-Xuan Jia; Multivariate weighted recurrence network analysis of EEG signals from ERP-based smart home system. Chaos 1 August 2018; 28 (8): 085713. https://doi.org/10.1063/1.5018824
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