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How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI

Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), t...

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Autores principales: Mano, Marsel, Lécuyer, Anatole, Bannier, Elise, Perronnet, Lorraine, Noorzadeh, Saman, Barillot, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359276/
https://www.ncbi.nlm.nih.gov/pubmed/28377691
http://dx.doi.org/10.3389/fnins.2017.00140
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author Mano, Marsel
Lécuyer, Anatole
Bannier, Elise
Perronnet, Lorraine
Noorzadeh, Saman
Barillot, Christian
author_facet Mano, Marsel
Lécuyer, Anatole
Bannier, Elise
Perronnet, Lorraine
Noorzadeh, Saman
Barillot, Christian
author_sort Mano, Marsel
collection PubMed
description Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies.
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spelling pubmed-53592762017-04-04 How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI Mano, Marsel Lécuyer, Anatole Bannier, Elise Perronnet, Lorraine Noorzadeh, Saman Barillot, Christian Front Neurosci Neuroscience Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies. Frontiers Media S.A. 2017-03-21 /pmc/articles/PMC5359276/ /pubmed/28377691 http://dx.doi.org/10.3389/fnins.2017.00140 Text en Copyright © 2017 Mano, Lécuyer, Bannier, Perronnet, Noorzadeh and Barillot. 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
Mano, Marsel
Lécuyer, Anatole
Bannier, Elise
Perronnet, Lorraine
Noorzadeh, Saman
Barillot, Christian
How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI
title How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI
title_full How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI
title_fullStr How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI
title_full_unstemmed How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI
title_short How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI
title_sort how to build a hybrid neurofeedback platform combining eeg and fmri
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359276/
https://www.ncbi.nlm.nih.gov/pubmed/28377691
http://dx.doi.org/10.3389/fnins.2017.00140
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