Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

Perronnet, Lorraine; Lécuyer, Anatole; Mano, Marsel; Bannier, Élise; Lotte, Fabien; Clerc, Maureen; Barillot, Christian

doi:10.3389/fnhum.2017.00193

Cited by 54 publications

(76 citation statements)

References 70 publications

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“…Preliminary experimental results are shown by Perronnet et al (2016). In the first study we compare the usage of bimodal NFB vs. unimodal NFB for self-regulation of brain activity of 8 subjects.…”

Section: Setup and Neurofeedback Experimentsmentioning

confidence: 83%

How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI

et al. 2017

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Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies.

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Section: Setup and Neurofeedback Experimentsmentioning

confidence: 83%

How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI

et al. 2017

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“…The proposed integrated 2D feedback consists of a ball moving on a 2D diamond-shape plot, the left dimension representing the EEG feature and the right dimension representing the fMRI feature. This type of feedback was introduced in our previous work (Perronnet et al, 2017) and we propose here an upgraded version in which the plot background delineates regions that indicate prefered direction of effort, encouraging the subject to regulate EEG and fMRI equitably. Our integrated 1D feedback consists of a ball moving in a gauge, the ball position representing the average of the EEG and fMRI features.…”

Section: Methodsmentioning

confidence: 99%

“…As a structural reference for the fMRI analysis, a high resolution 3D T1 MPRAGE sequence was acquired with the following parameters: TR/TI/TE = 1900/900/2.26ms, GRAPPA 2, FOV = 256 × 256mm 2 and 176 slabs, voxel size = 1 × 1 × 1mm 3 , flip angle = 90 • . Our multimodal EEG/fMRI-NF system (Mano et al 2017) integrates EEG and fMRI data streams via a TCP/IP socket. The EEG data is preprocessed with BrainVision Recview (Brain Products GmbH, Gilching, Germany) software for gradient and ballistocardiogram (BCG) artifact correction (see Section 2.4) and sent to Matlab (The MathWorks, Inc., Natick, Massachussets, United States) for further processing.…”

Section: Data Acquisitionmentioning

confidence: 99%

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Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Perronnet

Lécuyer

Mano

et al. 2018

Preprint

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Neurofeedback (NF) allows to exert self-regulation over specific aspects of one's own brain function by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, most commonly EEG or fMRI. EEG-fMRI-neurofeedback (EEG-fMRI-NF) is a new NF approach that consists of providing a NF based simultaneously on EEG and fMRI. By exploiting the complementarity of these two modalities, EEG-fMRI-NF opens a spectrum of possibilities for defining bimodal NF targets that could be more ro-bust, flexible and effective than unimodal ones. However facing a greater amount of information, the question arises of how to represent the EEG and fMRI features simultaneously in order to allow the subject to achieve better self-regulation. In this work, we propose that the EEG and fMRI features should be represented in a single bimodal feedback, which we refer to as integrated feedback. We then introduce two integrated feedback strategies for EEG-fMRI-NF and compare their early effects on a motor imagery task with a between-group design. The first group (BI DIM, n=10) was shown a two-dimensional (2D) feedback in which each dimension de-picted the information from one modality. The second group (UNI DIM, n=10) was shown a one-dimensional (1D) feedback that integrated both types of information even further by merging them into one. Online fMRI activations were significantly higher in the UNI DIM group than in the BI DIM group, which suggests that the 1D feedback is easier to control than the 2D feedback. However when looking at posthoc activation levels, the difference of fMRI activation levels between the NF runs and the preliminary motor imagery run without NF was more significant in the 2D group. Moreover, subjects from the BI DIM group produced more specific BOLD activations with a notably stronger activation in the right superior parietal lobule (BI DIM > UNI DIM, p < 0.001, uncorrected). These results suggest that the 2D feedback encourages subjects to explore their strategies to recruit more spe-cific brain patterns. To summarize, our study shows that 1D and 2D integrated feedbacks are effective but also appear to be complementary and could therefore be used in combination in a bimodal NF training program. Altogether, our study paves the way to novel integrated feedback strategies for the development of flexible and effective bimodal NF paradigms making the most of the bimodal information and better suited to clinical applications.

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“…Therefore, the rtfMRI‐nf with simultaneous EEG offers new opportunities for studying relationships between electrophysiological and hemodynamic processes in the human brain. This approach can also help to identify promising target measures for EEG neurofeedback (EEG‐nf) (Gruzelier, ), brain‐computer interfaces (Cavazza et al, ), and simultaneous multimodal rtfMRI‐EEG‐nf (Mano et al, ; Perronnet et al, ; Zotev, Phillips, Yuan, Misaki, & Bodurka, ).…”

Section: Introductionmentioning

confidence: 99%

Real‐time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm

Zotev

Misaki

Phillips

et al. 2017

Human Brain Mapping

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Real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG allows volitional modulation of BOLD activity of target brain regions and investigation of related electrophysiological activity. We applied this approach to study correlations between thalamic BOLD activity and alpha EEG rhythm. Healthy volunteers in the experimental group (EG, n=15) learned to upregulate BOLD activity of the target region consisting of the mediodorsal (MD) and anterior (AN) thalamic nuclei using rtfMRI-nf during retrieval of happy autobiographical memories. Healthy subjects in the control group (CG, n=14) were provided with a sham feedback. The EG participants were able to significantly increase BOLD activities of the MD and AN. Functional connectivity between the MD and the inferior precuneus was significantly enhanced during the rtfMRI-nf task. Average individual changes in the occipital alpha EEG power significantly correlated with the average MD BOLD activity levels for the EG. Temporal correlations between the occipital alpha EEG power and BOLD activities of the MD and AN were significantly enhanced, during the rtfMRI-nf task, for the EG compared to the CG. Temporal correlations with the alpha power were also significantly enhanced for the posterior nodes of the default mode network, including the precuneus/posterior cingulate, and for the dorsal striatum. Our findings suggest that the temporal correlation between the MD BOLD activity and posterior alpha EEG power is modulated by the interaction between the MD and the inferior precuneus, reflected in their functional connectivity. Our results demonstrate the potential of the rtfMRI-nf with simultaneous EEG for non-invasive neuromodulation studies of human brain function.

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Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

Cited by 54 publications

References 70 publications

How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI

How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Real‐time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm

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