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Learning bi-modal EEG-fMRI neurofeedback to improve neurofeedback in EEG only

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Abstract

Introduction In neurofeedback (NF), a new kind of data are available: electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) acquired simultaneously during bi-modal EEG-fMRI neurofeedback. These two complementary techniques have only recently been integrated in the context of NF for brain rehabilitation protocols. Bi-modal NF (NF-EEG-fMRI) combines information coming from two modalities sensitive to different aspect of brain activity, therefore providing a higher NF quality [1]. However, the use of the MRI scanner is cumbersome and exhausting for patients. We present, a novel methodological development, able to reduce the use of fMRI while providing to subjects NF-EEG sessions of quality comparable to the bi-modal NF sessions [2]. We propose an original alternative to the ill-posed problem of source reconstruction. We designed a non-linear model considering different frequency bands, electrodes and temporal delays, with a structured sparse regularisation. Results show that our model is able to significantly improve the quality of NF sessions over what EEG could provide alone. We tested our method on 17 subjects that performed three NF-EEG-fMRI sessions each.
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Dates and versions

inserm-02368720 , version 1 (18-11-2019)

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Claire Cury, Pierre Maurel, Giulia Lioi, Rémi Gribonval, Christian Barillot. Learning bi-modal EEG-fMRI neurofeedback to improve neurofeedback in EEG only. Real-Time Functional Imaging and Neurofeedback, Dec 2019, Maastricht, Netherlands. pp.1-2, ⟨10.1101/599589⟩. ⟨inserm-02368720⟩
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