Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern
Joint Authors
Wang, Xingyu
Jin, Jing
Cheng, Jiao
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-09
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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 (p < 0.05).
American Psychological Association (APA)
Cheng, Jiao& Jin, Jing& Wang, Xingyu. 2017. Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1139836
Modern Language Association (MLA)
Cheng, Jiao…[et al.]. Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1139836
American Medical Association (AMA)
Cheng, Jiao& Jin, Jing& Wang, Xingyu. Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1139836
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139836