Journal article
An open multi-center MEG-EEG dataset for studying conscious visual perception
- Abstract:
- Here, we present a large-scale, multi-center dataset of combined magnetoencephalographic(MEG) and electroencephalographic (EEG) recordings, along with eye-tracking dataandhigh-resolution structural MRI (T1); complementing with iEEG and fMRI datasets that areshared 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 includesrecordings 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-Gotarget 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 qualityof thedata was assessed and organized according to the Brain Imaging Data Structure (BIDS). It isaccompanied by extensive metadata to enhance reusability.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 4.3MB, Terms of use)
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(Supplementary materials, zip, 4.6MB, Terms of use)
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- Publisher copy:
- 10.1038/s41597-026-07350-9
Authors
+ Wellcome Trust
More from this funder
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 227420/Z/23/Z
+ Templeton World Charity Foundation
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- Funder identifier:
- https://ror.org/00x0z1472
- Grant:
- TWCF0389522
- TWCF0486
+ National Institute for Health and Care Research
More from this funder
- Funder identifier:
- https://ror.org/0187kwz08
- Grant:
- NIHR203316
- Publisher:
- Springer Nature
- Journal:
- Scientific Data More from this journal
- Volume:
- 13
- Article number:
- 799
- Publication date:
- 2026-05-29
- Acceptance date:
- 2026-04-23
- DOI:
- EISSN:
-
2052-4463
- Language:
-
English
- Pubs id:
-
2421098
- Local pid:
-
pubs:2421098
- Deposit date:
-
2026-05-18
- ARK identifier:
Terms of use
- Copyright holder:
- Liu et al.
- Copyright date:
- 2026
- Rights statement:
- © The Author(s) 2026. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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