Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain Computer Interfaces-an Offline Study.

Yawei Zhao,Xuejun Jiao,Minpeng Xu,Yong Cao,Hongzhi Qi,Dong Ming,Peng Zhou,Jiabei Tang

doi:10.3389/fnins.2018.00079

Abstract

Brain-computer interfaces (BCIs), independent of the brain's normal output pathways, are attracting an increasing amount of attention as devices that extract neural information. As a typical type of BCI system, the steady-state visual evoked potential (SSVEP)-based BCIs possess a high signal-to-noise ratio and information transfer rate. However, the current high speed SSVEP-BCIs were implemented with subjects concentrating on stimuli, and intentionally avoided additional tasks as distractors. This paper aimed to investigate how a distracting simultaneous task, a verbal n-back task with different mental workload, would affect the performance of SSVEP-BCI. The results from fifteen subjects revealed that the recognition accuracy of SSVEP-BCI was significantly impaired by the distracting task, especially under a high mental workload. The average classification accuracy across all subjects dropped by 8.67% at most from 1- to 4-back, and there was a significant negative correlation (maximum r = −0.48, p < 0.001) between accuracy and subjective mental workload evaluation of the distracting task. This study suggests a potential hindrance for the SSVEP-BCI daily use, and then improvements should be investigated in the future studies.

Highlights

Brain-computer interfaces (BCIs), which allow individuals to communicate independently of the brain’s normal output pathways of peripheral nerves and muscles, have attracted increasing amounts of attention in recent years
As the performances of state visual evoked potential (SSVEP)-BCIs are acceptable for application in some environments, we address them in this paper using a paradigm that adds another distracting task to an SSVEP-BCI
There is insufficient evidence of the presence of weakened SSVEP-BCI effects suffered by distracting task, and this is what we propose to investigate in this paper

Summary

Introduction

Brain-computer interfaces (BCIs), which allow individuals to communicate independently of the brain’s normal output pathways of peripheral nerves and muscles, have attracted increasing amounts of attention in recent years. Individuals with motor disabilities can control external devices effectively using BCI systems that decode different patterns of electroencephalography (EEG). As a typical EEG pattern in BCIs, a steady-state visual evoked potential (SSVEP) is elicited by a visual stimulus blinking at a frequency higher than 6 Hz (Vialatte et al, 2010; Luis Fernando and Jaime, 2012). When subjects use SSVEPBCIs, multiple flickers with different stimulation properties (e.g., frequency and phase) were shown. Estimation SSVEP-BCIs under Distracting Task in the screen, and each of them encodes a different command (e.g., wheelchair movement). Subjects select one of the commands by focusing on one of the flickering stimuli, and related frequency or phase components would be modulated in their recorded EEG. By analyzing the generated SSVEP, the BCI system tries to identify which stimulus the subject selected

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