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How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI.

Marsel Mano 1, * Anatole Lécuyer 1, 2 Elise Bannier 3, 2 Lorraine Perronnet 1 Saman Noorzadeh 1 Christian Barillot 1, 2, * 
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U1228, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : 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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Submitted on : Wednesday, August 23, 2017 - 11:54:44 AM
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Marsel Mano, Anatole Lécuyer, Elise Bannier, Lorraine Perronnet, Saman Noorzadeh, et al.. How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI.. Frontiers in Neuroscience, Frontiers, 2017, 11, pp.140. ⟨10.3389/fnins.2017.00140⟩. ⟨inserm-01576500⟩



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