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Classification of Movement and Inhibition Using a Hybrid BCI

Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)—when a person imagines a motion without executing it—is widely employed in BCI devices for motor control because of the endogenou...

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Autores principales: Chmura, Jennifer, Rosing, Joshua, Collazos, Steven, Goodwin, Shikha J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559436/
https://www.ncbi.nlm.nih.gov/pubmed/28860986
http://dx.doi.org/10.3389/fnbot.2017.00038
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author Chmura, Jennifer
Rosing, Joshua
Collazos, Steven
Goodwin, Shikha J.
author_facet Chmura, Jennifer
Rosing, Joshua
Collazos, Steven
Goodwin, Shikha J.
author_sort Chmura, Jennifer
collection PubMed
description Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)—when a person imagines a motion without executing it—is widely employed in BCI devices for motor control because of the endogenous origin of its neural control mechanisms, and the similarity in brain activation to actual movements. Challenges with translating a MI-BCI into a practical device used outside laboratories include the extensive training required, often due to poor user engagement and visual feedback response delays; poor user flexibility/freedom to time the execution/inhibition of their movements, and to control the movement type (right arm vs. left leg) and characteristics (reaching vs. grabbing); and high false positive rates of motion control. Solutions to improve sensorimotor activation and user performance of MI-BCIs have been explored. Virtual reality (VR) motor-execution tasks have replaced simpler visual feedback (smiling faces, arrows) and have solved this problem to an extent. Hybrid BCIs (hBCIs) implementing an additional control signal to MI have improved user control capabilities to a limited extent. These hBCIs either fail to allow the patients to gain asynchronous control of their movements, or have a high false positive rate. We propose an immersive VR environment which provides visual feedback that is both engaging and immediate, but also uniquely engages a different cognitive process in the patient that generates event-related potentials (ERPs). These ERPs provide a key executive function for the users to execute/inhibit movements. Additionally, we propose signal processing strategies and machine learning algorithms to move BCIs toward developing long-term signal stability in patients with distinctive brain signals and capabilities to control motor signals. The hBCI itself and the VR environment we propose would help to move BCI technology outside laboratory environments for motor rehabilitation in hospitals, and potentially for controlling a prosthetic.
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spelling pubmed-55594362017-08-31 Classification of Movement and Inhibition Using a Hybrid BCI Chmura, Jennifer Rosing, Joshua Collazos, Steven Goodwin, Shikha J. Front Neurorobot Neuroscience Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)—when a person imagines a motion without executing it—is widely employed in BCI devices for motor control because of the endogenous origin of its neural control mechanisms, and the similarity in brain activation to actual movements. Challenges with translating a MI-BCI into a practical device used outside laboratories include the extensive training required, often due to poor user engagement and visual feedback response delays; poor user flexibility/freedom to time the execution/inhibition of their movements, and to control the movement type (right arm vs. left leg) and characteristics (reaching vs. grabbing); and high false positive rates of motion control. Solutions to improve sensorimotor activation and user performance of MI-BCIs have been explored. Virtual reality (VR) motor-execution tasks have replaced simpler visual feedback (smiling faces, arrows) and have solved this problem to an extent. Hybrid BCIs (hBCIs) implementing an additional control signal to MI have improved user control capabilities to a limited extent. These hBCIs either fail to allow the patients to gain asynchronous control of their movements, or have a high false positive rate. We propose an immersive VR environment which provides visual feedback that is both engaging and immediate, but also uniquely engages a different cognitive process in the patient that generates event-related potentials (ERPs). These ERPs provide a key executive function for the users to execute/inhibit movements. Additionally, we propose signal processing strategies and machine learning algorithms to move BCIs toward developing long-term signal stability in patients with distinctive brain signals and capabilities to control motor signals. The hBCI itself and the VR environment we propose would help to move BCI technology outside laboratory environments for motor rehabilitation in hospitals, and potentially for controlling a prosthetic. Frontiers Media S.A. 2017-08-15 /pmc/articles/PMC5559436/ /pubmed/28860986 http://dx.doi.org/10.3389/fnbot.2017.00038 Text en Copyright © 2017 Chmura, Rosing, Collazos and Goodwin. 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) 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
Chmura, Jennifer
Rosing, Joshua
Collazos, Steven
Goodwin, Shikha J.
Classification of Movement and Inhibition Using a Hybrid BCI
title Classification of Movement and Inhibition Using a Hybrid BCI
title_full Classification of Movement and Inhibition Using a Hybrid BCI
title_fullStr Classification of Movement and Inhibition Using a Hybrid BCI
title_full_unstemmed Classification of Movement and Inhibition Using a Hybrid BCI
title_short Classification of Movement and Inhibition Using a Hybrid BCI
title_sort classification of movement and inhibition using a hybrid bci
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559436/
https://www.ncbi.nlm.nih.gov/pubmed/28860986
http://dx.doi.org/10.3389/fnbot.2017.00038
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