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Computational Intelligence and Neuroscience
Volume 2017, Article ID 1323985, 9 pages
Research Article

Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China

Correspondence should be addressed to Jing Jin; moc.liamg@tagnijnij

Received 18 October 2016; Revised 26 January 2017; Accepted 16 March 2017; Published 9 April 2017

Academic Editor: Mikhail A. Lebedev

Copyright © 2017 Jiao Cheng 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.


Brain-computer interface (BCI) systems allow users to communicate with the external world by recognizing the brain activity without the assistance of the peripheral motor nervous system. P300-based BCI is one of the most common used BCI systems that can obtain high classification accuracy and information transfer rate (ITR). Face stimuli can result in large event-related potentials and improve the performance of P300-based BCI. However, previous studies on face stimuli focused mainly on the effect of various face types (i.e., face expression, face familiarity, and multifaces) on the BCI performance. Studies on the influence of face transparency differences are scarce. Therefore, we investigated the effect of semitransparent face pattern (STF-P) (the subject could see the target character when the stimuli were flashed) and traditional face pattern (F-P) (the subject could not see the target character when the stimuli were flashed) on the BCI performance from the transparency perspective. Results showed that STF-P obtained significantly higher classification accuracy and ITR than those of F-P ( < 0.05).