Multirapid Serial Visual Presentation Framework for EEG-Based Target DetectionReport as inadecuate

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BioMed Research International - Volume 2017 2017, Article ID 2049094, 12 pages -

Research Article

China National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China

Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

Correspondence should be addressed to Bin Yan

Received 15 March 2017; Accepted 22 May 2017; Published 20 July 2017

Academic Editor: Pedro P. R. Filho

Copyright © 2017 Zhimin Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Target image detection based on a rapid serial visual presentation RSVP paradigm is a typical brain-computer interface system with various applications, such as image retrieval. In an RSVP paradigm, a P300 component is detected to determine target images. This strategy requires high-precision single-trial P300 detection methods. However, the performance of single-trial detection methods is relatively lower than that of multitrial P300 detection methods. Image retrieval based on multitrial P300 is a new research direction. In this paper, we propose a triple-RSVP paradigm with three images being presented simultaneously and a target image appearing three times. Thus, multitrial P300 classification methods can be used to improve detection accuracy. In this study, these mechanisms were extended and validated, and the characteristics of the multi-RSVP framework were further explored. Two different P300 detection algorithms were also utilized in multi-RSVP to demonstrate that the scheme is universally applicable. Results revealed that the detection accuracy of the multi-RSVP paradigm was higher than that of the standard RSVP paradigm. The results validate the effectiveness of the proposed method, and this method can provide a whole new idea in the field of EEG-based target detection.

Author: Zhimin Lin, Ying Zeng, Hui Gao, Li Tong, Chi Zhang, Xiaojuan Wang, Qunjian Wu, and Bin Yan



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