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A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality

A brain-computer interface (BCI) decodes the brain signals representing a desire to do something and transforms those signals into a control command. However, only a limited number of mental tasks have been previously investigated and classified. This study aimed to investigate two motor imagery (MI...

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Detalles Bibliográficos
Autores principales: Alchalabi, Bilal, Faubert, Jocelyn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803748/
https://www.ncbi.nlm.nih.gov/pubmed/31687005
http://dx.doi.org/10.1155/2019/2503431
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author Alchalabi, Bilal
Faubert, Jocelyn
author_facet Alchalabi, Bilal
Faubert, Jocelyn
author_sort Alchalabi, Bilal
collection PubMed
description A brain-computer interface (BCI) decodes the brain signals representing a desire to do something and transforms those signals into a control command. However, only a limited number of mental tasks have been previously investigated and classified. This study aimed to investigate two motor imagery (MI) commands, moving forward and moving backward, using a small number of EEG channels, to be used in a neurofeedback context. This study also aimed to simulate a BCI and investigate the offline classification between MI movements in forward and backward directions, using different features and classification methods. Ten healthy people participated in a two-session (48 min each) experiment. This experiment investigated neurofeedback of navigation in a virtual tunnel. Each session consisted of 320 trials where subjects were asked to imagine themselves moving in the tunnel in a forward or backward motion after a randomly presented (forward versus backward) command on the screen. Three electrodes were mounted bilaterally over the motor cortex. Trials were conducted with feedback. Data from session 1 were analyzed offline to train classifiers and to calculate thresholds for both tasks. These thresholds were used to form control signals that were later used online in session 2 in neurofeedback training to trigger the virtual tunnel to move in the direction requested by the user's brain signals. After 96 min of training, the online band-power neurofeedback training achieved an average classification of 76%, while the offline BCI simulation using power spectral density asymmetrical ratio and AR-modeled band power as features, and using LDA and SVM as classifiers, achieved an average classification of 80%.
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spelling pubmed-68037482019-11-04 A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality Alchalabi, Bilal Faubert, Jocelyn Comput Intell Neurosci Research Article A brain-computer interface (BCI) decodes the brain signals representing a desire to do something and transforms those signals into a control command. However, only a limited number of mental tasks have been previously investigated and classified. This study aimed to investigate two motor imagery (MI) commands, moving forward and moving backward, using a small number of EEG channels, to be used in a neurofeedback context. This study also aimed to simulate a BCI and investigate the offline classification between MI movements in forward and backward directions, using different features and classification methods. Ten healthy people participated in a two-session (48 min each) experiment. This experiment investigated neurofeedback of navigation in a virtual tunnel. Each session consisted of 320 trials where subjects were asked to imagine themselves moving in the tunnel in a forward or backward motion after a randomly presented (forward versus backward) command on the screen. Three electrodes were mounted bilaterally over the motor cortex. Trials were conducted with feedback. Data from session 1 were analyzed offline to train classifiers and to calculate thresholds for both tasks. These thresholds were used to form control signals that were later used online in session 2 in neurofeedback training to trigger the virtual tunnel to move in the direction requested by the user's brain signals. After 96 min of training, the online band-power neurofeedback training achieved an average classification of 76%, while the offline BCI simulation using power spectral density asymmetrical ratio and AR-modeled band power as features, and using LDA and SVM as classifiers, achieved an average classification of 80%. Hindawi 2019-10-09 /pmc/articles/PMC6803748/ /pubmed/31687005 http://dx.doi.org/10.1155/2019/2503431 Text en Copyright © 2019 Bilal Alchalabi and Jocelyn Faubert. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Alchalabi, Bilal
Faubert, Jocelyn
A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality
title A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality
title_full A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality
title_fullStr A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality
title_full_unstemmed A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality
title_short A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality
title_sort comparison between bci simulation and neurofeedback for forward/backward navigation in virtual reality
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803748/
https://www.ncbi.nlm.nih.gov/pubmed/31687005
http://dx.doi.org/10.1155/2019/2503431
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