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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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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. |
format | Online Article Text |
id | pubmed-5359276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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