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Impact of 1D and 2D visualisation on EEG-fMRI neurofeedback training during a motor imagery task.

Claire Cury 1, 2 Giulia Lioi 1, 3 Lorraine Perronnet 3 Anatole Lécuyer 3 Pierre Maurel 1 Christian Barillot 1
1 Empenn
INSERM - Institut National de la Santé et de la Recherche Médicale, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
3 Hybrid - 3D interaction with virtual environments using body and mind
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : Bi-modal EEG-fMRI neurofeedback (NF) is a new technique of great interest. First, it can improve the quality of NF training by combining different real-time information (haemody-namic and electrophysiological) from the participant's brain activity; Second, it has potential to better understand the link and the synergy between the two modalities (EEG-fMRI). However there are different ways to show to the participant his NF scores during bi-modal NF sessions. To improve data fusion methodologies, we investigate the impact of a 1D or 2D representation when a visual feedback is given during motor imagery task. Results show a better synergy between EEG and fMRI when a 2D display is used. Subjects have better fMRI scores when 1D is used for bi-modal EEG-fMRI NF sessions; on the other hand, they regulate EEG more specifically when the 2D metaphor is used.
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https://www.hal.inserm.fr/inserm-02489459
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Submitted on : Wednesday, March 18, 2020 - 7:36:25 PM
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Claire Cury, Giulia Lioi, Lorraine Perronnet, Anatole Lécuyer, Pierre Maurel, et al.. Impact of 1D and 2D visualisation on EEG-fMRI neurofeedback training during a motor imagery task.. IEEE International Symposium on Biomedical Imaging, Apr 2020, Iowa City, United States. ⟨inserm-02489459v2⟩

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