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Combining EEG and FMRI for Neurofeedback

Lorraine Perronnet 1, 2
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique , INSERM - Institut National de la Santé et de la Recherche Médicale : U1228
2 Hybrid - 3D interaction with virtual environments using body and mind
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : NF is the process of feeding back real-time information to an individual about his/her ongoing brain activity, so that he/she can train to self-regulate neural substrates of specific behavioral functions. NF has been extensively studied for brain rehabilitation of patients with psychiatric and neurological disorders. However its effective deployment in the clinical armamentarium is being held back by the lack of evidence about its efficacy. One of the possible reason for the debated efficacy of current approaches could be the inherent limitations of single imaging modalities. Indeed, most NF approaches rely on the use of a single modality, EEG and fMRI being the two most widely used. While EEG is inexpensive and benefits from a high tem- poral resolution (millisecond), its spatial resolution (centimeters) is limited by volume conduction of the head and the number of electrodes. Also source lo- calization from EEG is inaccurate because of the ill-posed inverse problem. In a complementary way, fMRI gives access to the self-regulation of specific brain regions at high spatial resolution (millimeter) but has low temporal resolution (second). Combined EEG-fMRI has proven much valuable for the study of human brain function, however it has rarely been exploited for NF purpose. In the con- text of NF, combining EEG and fMRI enables cross-modal paradigm evaluation and validation. But more interestingly it opens up avenues for the development of new NF approaches that would mix both modalities, either at the calibration phase or to provide a bimodal NF signal. Combined EEG-fMRI poses numer- ous challenges with regard to basic physiology, study design, data quality, analy- sis/integration and interpretation. These challenges are even greater if EEG and fMRI are both to be used simultaneously for online NF computation, because of the real-time constraint and the difficulty to find a task design compatible with EEG and fMRI’ diverging natures. The theoretical part of this PhD dissertation aims at identifying methodologi- cal aspects that differ between EEG-NF and fMRI-NF and at examining the mo- tivations and strategies for combining EEG and fMRI for NF purpose. Among these combination strategies, we choose to focus on bimodal EEG-fMRI-NF as it seems to be one of the most promising approach and is mostly unexplored. The feasibility of this approach was recently demonstrated and opened an en- tire new field of investigation. First and foremost, we would like to address the following questions: what is the added value of bimodal NF over unimodal NF; are there any specific mechanisms involved when learning to control two NF signals simultaneously; how to integrate EEG and fMRI to derive a single feed- back ? The experimental part of this PhD dissertation therefore focuses on the development and evaluation of methods for bimodal EEG-fMRI-NF. In order to conduct bimodal NF experiments, we start by building up a real-time EEG- fMRI platform. Then in a first study, we compare for the first time bimodal EEG-fMRI-NF with unimodal EEG-NF and fMRI-NF. Eventually, in a second study, we introduce and evaluate two integrated feedback strategies for EEG- fMRI-NF.
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Submitted on : Monday, October 2, 2017 - 11:28:24 AM
Last modification on : Friday, October 8, 2021 - 6:50:32 PM


  • HAL Id : tel-01598667, version 1


Lorraine Perronnet. Combining EEG and FMRI for Neurofeedback. Human-Computer Interaction [cs.HC]. University of Rennes I, 2017. English. ⟨tel-01598667⟩



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