A P300-Based Brain-Computer Interface for Improving Attention

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...

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Main Authors: Mahnaz Arvaneh, Ian H. Robertson, Tomas E. Ward
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-01-01
Series:Frontiers in Human Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fnhum.2018.00524/full
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spelling doaj-c41e004c5c214d2bbf0f114d11fb5c472020-11-25T03:22:50ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612019-01-011210.3389/fnhum.2018.00524424985A P300-Based Brain-Computer Interface for Improving AttentionMahnaz Arvaneh0Ian H. Robertson1Tomas E. Ward2Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United KingdomGlobal Brain Health Institute, Institute of Neuroscience, Trinity College Dublin, Dublin, IrelandInsight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, IrelandA 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.https://www.frontiersin.org/article/10.3389/fnhum.2018.00524/fullbrain-computer interfaceneurofeedbackP300attentionelectroencephalography
collection DOAJ
language English
format Article
sources DOAJ
author Mahnaz Arvaneh
Ian H. Robertson
Tomas E. Ward
spellingShingle Mahnaz Arvaneh
Ian H. Robertson
Tomas E. Ward
A P300-Based Brain-Computer Interface for Improving Attention
Frontiers in Human Neuroscience
brain-computer interface
neurofeedback
P300
attention
electroencephalography
author_facet Mahnaz Arvaneh
Ian H. Robertson
Tomas E. Ward
author_sort Mahnaz Arvaneh
title A P300-Based Brain-Computer Interface for Improving Attention
title_short A P300-Based Brain-Computer Interface for Improving Attention
title_full A P300-Based Brain-Computer Interface for Improving Attention
title_fullStr A P300-Based Brain-Computer Interface for Improving Attention
title_full_unstemmed A P300-Based Brain-Computer Interface for Improving Attention
title_sort p300-based brain-computer interface for improving attention
publisher Frontiers Media S.A.
series Frontiers in Human Neuroscience
issn 1662-5161
publishDate 2019-01-01
description 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.
topic brain-computer interface
neurofeedback
P300
attention
electroencephalography
url https://www.frontiersin.org/article/10.3389/fnhum.2018.00524/full
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