The Self-Face Paradigm Improves the Performance of the P300-Speller System.

Zhaohua Lu,Ning Gao,Jingjing Yang,Qi Li

doi:10.3389/fncom.2019.00093

Zhaohua Lu, Ning Gao + Show 2 more

Open Access

https://doi.org/10.3389/fncom.2019.00093

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Abstract

Objective: Previous studies have shown that the performance of the famous face P300-speller was better than that of the classical row/column flashing P300-speller. Furthermore, in some studies, the brain was more active when responding to one's own face than to a famous face, and a self-face stimulus elicited larger amplitude event-related potentials (ERPs) than did a famous face. Thus, we aimed to study the role of the self-face paradigm on further improving the performance of the P300-speller system with the famous face P300-speller paradigm as the control paradigm.Methods: We designed two facial P300-speller paradigms based on the self-face and a famous face (Ming Yao, a sports star; the famous face spelling paradigm) with a neutral expression.Results: ERP amplitudes were significantly greater in the self-face than in the famous face spelling paradigm at the parietal area from 340 to 480 ms (P300), from 480 to 600 ms (P600f), and at the fronto-central area from 700 to 800 ms. Offline and online classification results showed that the self-face spelling paradigm accuracies were significantly higher than those of the famous face spelling paradigm at superposing first two times (P < 0.05). Similar results were found for information transfer rates (P < 0.05).Conclusions: The self-face spelling paradigm significantly improved the performance of the P300-speller system. This has significant practical applications for brain-computer interfaces (BCIs) and could avoid infringement issues caused by using images of other people's faces.

Highlights

A brain-computer interface (BCI) is a communication technology based on brain activity
Feature differences in the event-related potential (ERP) elicited by target and nontarget stimuli in the famous face and self-face spelling paradigms were indicated by the r-squared values (Figure 5)
The results of classification accuracies based on feature A and feature B are shown in Figure 7, which shows the average accuracies across all subjects at each sequence in famous face and self-face spelling paradigms

Summary

Introduction

A brain-computer interface (BCI) is a communication technology based on brain activity. BCIs allow severely disabled patients, especially patients with amyotrophic lateral sclerosis, to send messages or control external devices without physical actions (Thompson et al, 2013; Rosenfeld and Wong, 2017; Lazarou et al, 2018). BCIs can help restore function in patients with severe motor disabilities, including patients with spinal cord injury, stroke, neuromuscular disorder, and limb amputation (Takeuchi et al, 2015; Carelli et al, 2017; Wang et al, 2019). Some studies have used BCIs for enhancing clinical communication assessments in patients with disorders of consciousness (Wang et al, 2017; Jeunet et al, 2018). BCIs are commonly based on electroencephalogram (EEG) that is recorded non-invasively via electrodes placed on the surface of the head (Waldert, 2016).

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