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Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns

Li, Shurui and Jin, Jing and Daly, Ian and Zuo, Cili and Wang, Xingyu and Cichocki, Andrzej (2020) 'Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns.' Frontiers in Neuroscience, 14. ISSN 1662-453X

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Objective: Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns. Methods: We compared three patterns in which semitransparent face is overlaid three primary colors as stimuli: red semitransparent face (RSF), green semitransparent face (GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. In addition, a Repeated-measures ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis. Results: The results indicated that the RSF pattern achieved the highest online averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the lowest performance was caused by the BSF pattern with an accuracy of 81.39%. Furthermore, significant differences in classification accuracy and ITR were found between RSF and GSF (p < 0.05) and between RSF and BSF patterns (p < 0.05). Conclusion: The semitransparent faces colored red (RSF) pattern yielded the best performance of the three patterns. The proposed patterns based on ERP-BCI system have a clinically significant impact by increasing communication speed and accuracy of the P300-speller for patients with severe motor impairment.

Item Type: Article
Uncontrolled Keywords: brain-computer interface, ERP, chromatic stimuli, semitransparent face, visual stimuli
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Feb 2020 10:34
Last Modified: 15 Jan 2022 01:32

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