Li Shurui, Jin Jing, Daly Ian, Zuo Cili, Wang Xingyu, Cichocki Andrzej
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China.
Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.
Front Neurosci. 2020 Jan 31;14:54. doi: 10.3389/fnins.2020.00054. eCollection 2020.
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.
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.
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 ( < 0.05) and between RSF and BSF patterns ( < 0.05).
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.
先前的研究表明，结合颜色属性可作为向脑机接口（BCI）用户呈现的显示内容的一部分，以提高性能。在此基础上，我们探讨了面部刺激与三种原色（RGB）的组合对BCI性能的影响，该性能通过分类准确率和信息传输率（ITR）进行评估。此外，我们分析了三种模式的波形。
我们比较了三种模式，其中半透明面部叠加三种原色作为刺激：红色半透明面部（RSF）、绿色半透明面部（GSF）和蓝色半透明面部（BSF）。使用贝叶斯线性判别分析（BLDA）构建个体分类器模型。此外，选择重复测量方差分析（RM-ANOVA）和邦费罗尼校正进行统计分析。
结果表明，RSF模式实现了最高的在线平均准确率，为93.89%，其次是GSF模式，为87.78%，而最低性能是由BSF模式导致的，准确率为81.39%。此外，在RSF和GSF之间（<0.05）以及RSF和BSF模式之间（<0.05）发现了分类准确率和ITR的显著差异。
红色半透明面部（RSF）模式在三种模式中表现最佳。所提出的基于事件相关电位脑机接口（ERP-BCI）系统的模式通过提高严重运动障碍患者的P300拼写器的通信速度和准确性，具有临床显著影响。
