A P300-Based Brain-Computer Interface for Improving Attention.

Mahnaz Arvaneh,Tomas E Ward,Ian H Robertson

doi:10.3389/fnhum.2018.00524

Mahnaz Arvaneh, Tomas E Ward + Show 1 more

Open Access

https://doi.org/10.3389/fnhum.2018.00524

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Abstract

A Brain-computer Interface (BCI) can be used as a neurofeedback training tool to improve cognitive performance. BCIs aim to improve the effectiveness and efficiency of the conventional neurofeedback methods by focusing on the self-regulation of individualized neuromarkers rather than generic ones in a graphically appealing training environment. In this work, for the first time, we have modified a widely used P300-based speller BCI and used it as an engaging neurofeedack training game to enhance P300. According to the user's performance the game becomes more difficult in an adaptive manner, requiring the generation of a larger and stronger P300 (i.e., in terms of total energy) in response to target stimuli. Since the P300 is generated naturally without conscious effort in response to a target trial, unlike many rhythm-based neurofeedback tools, the ability to control the proposed P300-based neurofeedback training is obtained after a short calibration without undergoing tedious trial and error sessions. The performance of the proposed neurofeedback training was evaluated over a short time scale (approximately 30 min training) using 28 young adult participants who were randomly assigned to either the experimental group or the control group. In summary, our results show that the proposed P300-based BCI neurofeedback training yielded a significant enhancement in the ERP components of the target trials (i.e., 150–550 ms after the onset of stimuli which includes P300) as well as attenuation in the corresponding ERP components of the non-target trials. In addition, more centro-parietal alpha suppression was observed in the experimental group during the neurofeedback training as well as a post-training spatial attention task. Interestingly, a significant improvement in the response time of a spatial attention task performed immediately after the neurofeedback training was observed in the experimental group. This paper, as a proof-of-concept study, suggests that the proposed neurofeedback training tool is a promising tool for improving attention particularly for those who are at risk of attention deficiency.

Highlights

Brain-computer Interface (BCI) provides a direct communication pathway between the human’s brain and an external device (Wolpaw et al, 2002; Birbaumer, 2007)
This study proposes a new version of the P300-based speller BCI as an interactive neurofeedback training game, such that according to the user’s performance the game becomes more difficult requiring the generation of a larger amplitude P300 for target stimuli
Changes in Event-related potential (ERP) components of EEG data To evaluate whether the provided feedback changes EEG patterns in the P300-based speller task, we focused on the total powers of the target and non-target trials calculated from 150 ms to 550 ms after the onset of the stimuli

Summary

Introduction

Brain-computer Interface (BCI) provides a direct communication pathway between the human’s brain and an external device (Wolpaw et al, 2002; Birbaumer, 2007). Using appropriate sensors and computational algorithms, BCI analyses brain signals, extracts relevant brain patterns and translates them to control signals. P300-Based BCI for Neurofeedback Training paralyzed people, helping them gain some sort of independence by controlling assistive devices using their thoughts (Wolpaw et al, 2002; Birbaumer, 2006). BCI has been suggested as a potential neurofeedback training tool for improving cognitive performance (Lim et al, 2012; van Erp et al, 2012; Tih-Shih et al, 2013; Yang et al, 2018)

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