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A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface.

Authors
  • Li, Wei1
  • Li, Mengfan2
  • Zhou, Huihui3
  • Chen, Genshe4
  • Jin, Jing5
  • Duan, Feng6
  • 1 1 Department of Computer & Electrical Engineering, and Computer Science, California State University, Bakersfield, California 93311, USA.
  • 2 2 State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China. , (China)
  • 3 3 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, P. R. China. , (China)
  • 4 4 Intelligent Fusion Technology, Germantown 41061, USA.
  • 5 5 East China University of Science and Technology, Shanghai 200237, P. R. China. , (China)
  • 6 6 College of Computer and Control Engineering, Nankai University, Tianjin 300071, P. R. China. , (China)
Type
Published Article
Journal
International journal of neural systems
Publication Date
Jul 26, 2018
Pages
1850034–1850034
Identifiers
DOI: 10.1142/S012906571850034X
PMID: 30185104
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

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