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

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).
• 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.