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Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications

Brain–machine interfaces (BMIs) using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenous cognitive activit...

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Autores principales: Astrand, Elaine, Wardak, Claire, Ben Hamed, Suliann
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4130369/
https://www.ncbi.nlm.nih.gov/pubmed/25161613
http://dx.doi.org/10.3389/fnsys.2014.00144
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author Astrand, Elaine
Wardak, Claire
Ben Hamed, Suliann
author_facet Astrand, Elaine
Wardak, Claire
Ben Hamed, Suliann
author_sort Astrand, Elaine
collection PubMed
description Brain–machine interfaces (BMIs) using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenous cognitive activity, also called cognitive BMIs. While more challenging, this approach opens new dimensions to the rehabilitation of cognitive disorders. In the present work, we focus on BMIs driven by visuospatial attention signals and we provide a critical review of these studies in the light of the accumulated knowledge about the psychophysics, anatomy, and neurophysiology of visual spatial attention. Importantly, we provide a unique comparative overview of the several studies, ranging from non-invasive to invasive human and non-human primates studies, that decode attention-related information from ongoing neuronal activity. We discuss these studies in the light of the challenges attention-driven cognitive BMIs have to face. In a second part of the review, we discuss past and current attention-based neurofeedback studies, describing both the covert effects of neurofeedback onto neuronal activity and its overt behavioral effects. Importantly, we compare neurofeedback studies based on the amplitude of cortical activity to studies based on the enhancement of cortical information content. Last, we discuss several lines of future research and applications for attention-driven cognitive brain-computer interfaces (BCIs), including the rehabilitation of cognitive deficits, restored communication in locked-in patients, and open-field applications for enhanced cognition in normal subjects. The core motivation of this work is the key idea that the improvement of current cognitive BMIs for therapeutic and open field applications needs to be grounded in a proper interdisciplinary understanding of the physiology of the cognitive function of interest, be it spatial attention, working memory or any other cognitive signal.
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spelling pubmed-41303692014-08-26 Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications Astrand, Elaine Wardak, Claire Ben Hamed, Suliann Front Syst Neurosci Neuroscience Brain–machine interfaces (BMIs) using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenous cognitive activity, also called cognitive BMIs. While more challenging, this approach opens new dimensions to the rehabilitation of cognitive disorders. In the present work, we focus on BMIs driven by visuospatial attention signals and we provide a critical review of these studies in the light of the accumulated knowledge about the psychophysics, anatomy, and neurophysiology of visual spatial attention. Importantly, we provide a unique comparative overview of the several studies, ranging from non-invasive to invasive human and non-human primates studies, that decode attention-related information from ongoing neuronal activity. We discuss these studies in the light of the challenges attention-driven cognitive BMIs have to face. In a second part of the review, we discuss past and current attention-based neurofeedback studies, describing both the covert effects of neurofeedback onto neuronal activity and its overt behavioral effects. Importantly, we compare neurofeedback studies based on the amplitude of cortical activity to studies based on the enhancement of cortical information content. Last, we discuss several lines of future research and applications for attention-driven cognitive brain-computer interfaces (BCIs), including the rehabilitation of cognitive deficits, restored communication in locked-in patients, and open-field applications for enhanced cognition in normal subjects. The core motivation of this work is the key idea that the improvement of current cognitive BMIs for therapeutic and open field applications needs to be grounded in a proper interdisciplinary understanding of the physiology of the cognitive function of interest, be it spatial attention, working memory or any other cognitive signal. Frontiers Media S.A. 2014-08-12 /pmc/articles/PMC4130369/ /pubmed/25161613 http://dx.doi.org/10.3389/fnsys.2014.00144 Text en Copyright © 2014 Astrand, Wardak and Ben Hamed. http://creativecommons.org/licenses/by/3.0/ 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) or licensor 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
Astrand, Elaine
Wardak, Claire
Ben Hamed, Suliann
Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications
title Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications
title_full Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications
title_fullStr Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications
title_full_unstemmed Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications
title_short Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications
title_sort selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4130369/
https://www.ncbi.nlm.nih.gov/pubmed/25161613
http://dx.doi.org/10.3389/fnsys.2014.00144
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