Automated Electrodes Detection during simultaneous EEG/fMRI

Mathis Fleury 1 Christian Barillot 2 Elise Bannier 3 Marsel Mano 4 Pierre Maurel 2
1 VERIDIS - Modeling and Verification of Distributed Algorithms and Systems
Inria Nancy - Grand Est, LORIA - FM - Department of Formal Methods
2 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
3 CHU Pontchaillou [Rennes]
4 Hybrid - 3D interaction with virtual environments using body and mind
Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
Abstract : The coupling of EEG/fMRI allows measuring brain activity at high spatio-temporal resolution. The localisation of EEG sources depends on several parameters including the position of the electrodes on the scalp. An accurate knowledge about this information is important for source reconstruction. Currently, when acquiring EEG/fMRI together, the position of the electrodes is generally estimated according to fiducial points by using a template. In the context of simultaneous EEG/fMRI acquisition, a natural idea is to use MR images to localise EEG electrodes. However, MR compatible electrodes are built to be almost invisible on MR Images. Taking advantage of a recently proposed Ultra-Short Echo Time (UTE) sequence, we introduce a fully automatic method to detect and label those electrodes in MR images. Our method was tested on 8 subjects wearing a 64-channel EEG cap. This automated method showed an average detection accuracy of 94% and the average position error was 3.1 mm. These results suggest that the proposed method has potential for determining the position of the electrodes during simultaneous EEG/fMRI acquisition.
Type de document :
Article dans une revue
Frontiers in information and communication technologies, Frontiers Media S.A., In press
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https://hal.inria.fr/hal-01939735
Contributeur : Mathis Fleury <>
Soumis le : jeudi 29 novembre 2018 - 16:59:32
Dernière modification le : jeudi 13 décembre 2018 - 11:59:13

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  • HAL Id : hal-01939735, version 1

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CENTRALESUPELEC | IRISA | INRIA | UNIV-RENNES | UR1-UFR-ISTIC | UNIV-LORRAINE | LORIA | LORIA-FM

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Mathis Fleury, Christian Barillot, Elise Bannier, Marsel Mano, Pierre Maurel. Automated Electrodes Detection during simultaneous EEG/fMRI. Frontiers in information and communication technologies, Frontiers Media S.A., In press. 〈hal-01939735〉

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