Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration

• Published in: Scientific data Vol. 7; no. 1; pp. 173 - 15
• Main Authors: Lioi, Giulia, Cury, Claire, Perronnet, Lorraine, Mano, Marsel, Bannier, Elise, Lécuyer, Anatole, Barillot, Christian
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.06.2020; Nature Publishing Group
• Subjects: 631/378; 639/166/985; 692/700/1421/65; Bioengineering; Biofeedback; Brain Mapping; Cognitive science; Cognitive Sciences; Computer Science; Data Descriptor; Datasets; EEG; Electroencephalography; Feedback; Functional magnetic resonance imaging; Healthy Volunteers; Humanities and Social Sciences; Humans; Imaging; Life Sciences; Magnetic Resonance Imaging; Medical Imaging; Mental task performance; multidisciplinary; Neurofeedback; Neuroimaging; Neurons and Cognition; Neuroscience; Science; Science (multidisciplinary); Sensory integration
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-020-0498-3

Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. This represents one of the first simultaneous recording of EEG and fMRI for NF and here we present the first open access bi-modal NF dataset integrating EEG and fMRI. We believe that it will be a valuable tool to (1) advance and test methodologies for multi-modal data integration, (2) improve the quality of NF provided, (3) improve methodologies for de-noising EEG acquired under MRI and (4) investigate the neuromarkers of motor-imagery using multi-modal information. Measurement(s) brain measurement • motor behavior Technology Type(s) functional magnetic resonance imaging • electroencephalography (EEG) Factor Type(s) task • subject Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12251765