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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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Autores principales: Arvaneh, Mahnaz, Robertson, Ian H., Ward, Tomas E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328468/
https://www.ncbi.nlm.nih.gov/pubmed/30662400
http://dx.doi.org/10.3389/fnhum.2018.00524
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author Arvaneh, Mahnaz
Robertson, Ian H.
Ward, Tomas E.
author_facet Arvaneh, Mahnaz
Robertson, Ian H.
Ward, Tomas E.
author_sort Arvaneh, Mahnaz
collection PubMed
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.
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spelling pubmed-63284682019-01-18 A P300-Based Brain-Computer Interface for Improving Attention Arvaneh, Mahnaz Robertson, Ian H. Ward, Tomas E. Front Hum Neurosci Neuroscience 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. Frontiers Media S.A. 2019-01-04 /pmc/articles/PMC6328468/ /pubmed/30662400 http://dx.doi.org/10.3389/fnhum.2018.00524 Text en Copyright © 2019 Arvaneh, Robertson and Ward. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Arvaneh, Mahnaz
Robertson, Ian H.
Ward, Tomas E.
A P300-Based Brain-Computer Interface for Improving Attention
title 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_short A P300-Based Brain-Computer Interface for Improving Attention
title_sort p300-based brain-computer interface for improving attention
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328468/
https://www.ncbi.nlm.nih.gov/pubmed/30662400
http://dx.doi.org/10.3389/fnhum.2018.00524
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