An open multi-center MEG-EEG dataset for studying conscious visual perception

Here, we present a large-scale, multi-center dataset of combined magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, along with eye-tracking data and high-resolution structural MRI (T1); complementing with iEEG and fMRI datasets that are shared in accompanying data papers. The data was obtained through an adversarial collaboration between advocates of two neuroscientific theories of consciousness: the Global Neuronal Workspace Theory and the Integrated Information Theory. The dataset includes recordings from 100 individuals (mean age 22.79 ± 3.59 years, 54 female, all right-handed) across two research centers (UK and China), using a standardized data collection protocol. During the experiment, participants were asked to perform a non-speeded Go/No-Go target detection task, during which they were exposed to visual stimuli from four distinct categories (faces, objects, letters, false fonts) presented at different orientations (front, left, right view), and for varying durations (0.5, 1.0, 1.5 s), under different task conditions. The quality of the data was assessed and organized according to the Brain Imaging Data Structure (BIDS). It is accompanied by extensive metadata to enhance reusability.

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Publication Details

Journal
Scientific Data
Published
2026-05-29
DOI
https://doi.org/10.1038/s41597-026-07350-9
Primary Topic
Face Recognition and Perception
Type
article
Field-Weighted Citation Impact
0.00

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article

An open multi-center MEG-EEG dataset for studying conscious visual perception

Fatemeh Taheriyan, Lucía Melloni, Diptyajit Das, Praveen Sripad et al.
Scientific Data
Face Recognition and Perception
article

An open multi-center MEG-EEG dataset for studying conscious visual perception

Fatemeh Taheriyan, Lucía Melloni, Diptyajit Das, Praveen Sripad, Tara Ghafari, Tanya Brown, Urszula Górska, Oscar Ferrante, Rony Hirschhorn, Niccolò Bonacchi, Dorottya Hetenyi, Kyle Kahraman, Michael Pitts, Ole Jensen, Liad Mudrik, Shujun Yang, Ling Liu, Huan Luo
article en

Abstract

Here, we present a large-scale, multi-center dataset of combined magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, along with eye-tracking data and high-resolution structural MRI (T1); complementing with iEEG and fMRI datasets that are shared in accompanying data papers. The data was obtained through an adversarial collaboration between advocates of two neuroscientific theories of consciousness: the Global Neuronal Workspace Theory and the Integrated Information Theory. The dataset includes recordings from 100 individuals (mean age 22.79 ± 3.59 years, 54 female, all right-handed) across two research centers (UK and China), using a standardized data collection protocol. During the experiment, participants were asked to perform a non-speeded Go/No-Go target detection task, during which they were exposed to visual stimuli from four distinct categories (faces, objects, letters, false fonts) presented at different orientations (front, left, right view), and for varying durations (0.5, 1.0, 1.5 s), under different task conditions. The quality of the data was assessed and organized according to the Brain Imaging Data Structure (BIDS). It is accompanied by extensive metadata to enhance reusability.

Scientific DataVol. 13(1)
Canadian Institute for Advanced Research (CA), Champalimaud Foundation (PT), Beijing Language and Culture University (CN), University of Wisconsin–Madison (US), Tel Aviv University (IL), Queen Mary University of London (GB), Peking University (CN), University of Surrey (GB), University of Oxford (GB), Max Planck Institute for Empirical Aesthetics (DE), Wellcome Centre for Integrative Neuroimaging (GB), ISPA - Instituto Universitário (PT), Cardiovascular Center Frankfurt (DE), University College London (GB), Reed College (US), New York University (US), University of Birmingham (GB), University of Amsterdam (NL), Ruhr University Bochum (DE)
Wellcome, National Institute for Health and Care Research, Max-Planck-Gesellschaft, Templeton World Charity Foundation
Openalex Percentile: Top 5%
Face Recognition and Perception
DOIOpen Access PDFOpenAlex
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