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Communication Dans Un Congrès Année : 2020

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

Résumé

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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Dates et versions

inserm-02489459 , version 1 (24-02-2020)
inserm-02489459 , version 2 (18-03-2020)

Identifiants

  • HAL Id : inserm-02489459 , version 1

Citer

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-02489459v1⟩
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