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

Ling Liu^1,2,3, Oscar Ferrante^4,5, Tara Ghafari^4,6,7

¹Cognitive Science and Allied Health School, Beijing Language and Culture University, Beijing, 100875, China.

Scientific Data

|May 29, 2026

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Summary

This study introduces a large, multi-center dataset combining magnetoencephalography (MEG) and electroencephalography (EEG) recordings. The data supports research into consciousness theories, featuring diverse stimuli and standardized collection.

Area of Science:

Neuroscience
Cognitive Science
Neuroimaging

Background:

Investigating consciousness requires robust neuroimaging data.
Two prominent theories, Global Neuronal Workspace Theory and Integrated Information Theory, offer different perspectives on consciousness.
A comprehensive dataset is needed to compare these theories.

Purpose of the Study:

To present a large-scale, multi-center dataset of combined magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings.
To facilitate research comparing the Global Neuronal Workspace Theory and Integrated Information Theory.
To provide high-quality, standardized neuroimaging data with extensive metadata for reusability.

Main Methods:

Collected combined MEG and EEG data from 100 participants across two centers (UK and China).

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Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

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Included eye-tracking and high-resolution structural MRI (T1); iEEG and fMRI data are in accompanying papers.

Participants performed a Go/No-Go target detection task with varied visual stimuli (faces, objects, letters, false fonts) and parameters.

Data quality assessed and organized using the Brain Imaging Data Structure (BIDS) standard.

Main Results:

A comprehensive dataset of MEG, EEG, eye-tracking, and MRI data was successfully collected and curated.
The dataset adheres to the Brain Imaging Data Structure (BIDS) for enhanced interoperability and reusability.
Extensive metadata accompanies the dataset, detailing experimental conditions and participant information.

Conclusions:

This dataset provides a valuable resource for neuroscientists studying consciousness.
The data enables direct comparison and testing of predictions from the Global Neuronal Workspace Theory and Integrated Information Theory.
Standardized collection and BIDS compliance ensure the dataset's utility for future research and replication.