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

Shurui Li,Andrzej Cichocki,Jing Jin,Cili Zuo,Ian Daly,Xingyu Wang

doi:10.3389/fnins.2020.00054

Abstract

ObjectivePrevious 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.MethodsWe 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.ResultsThe 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).ConclusionThe 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.

Highlights

Brain-computer interface (BCI) systems enable their users to achieve direct communication with others or the outside environment by brain activity alone, independent of muscle control
Four color blocks lie around the peak point, which represents four types of potentials including the vertex positive potential (VPP), the N200, P300, and N400 potentials
We combined chromatic difference (RGB) with semitransparent face stimuli to explore the performance of different colored stimuli patterns in an event-related potentials (ERPs) based BCI system

Summary

Introduction

Brain-computer interface (BCI) systems enable their users to achieve direct communication with others or the outside environment by brain activity alone, independent of muscle control. The brain activity used to control a BCI can be measured using different signal acquisition approaches such as electroencephalogram (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), electrocorticogram (ECoG), or near infrared spectroscopy (NIRS) (Vidal, 1973, 1977; Wolpaw et al, 2002). Since EEG signals are recorded via non-invasive electrodes placed on the surface of the scalp, EEG-based BCI systems are very commonly used. Three key signal components of the EEG are frequently used for BCI control: event-related potentials (ERPs), steadystate visual evoked potentials (SSVEP), and motor imagery (MI) (Sutton et al, 1965; Coles and Rugg, 1995). The focus of the present study is the ERP-based BCI

Methods

Results

Discussion

Conclusion