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References ---------- Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfo

Participants

34

Size

92.3 GB

Updated

Jun 6, 2026

Participants

28

Size

218 GB

Updated

Jun 6, 2026

This dataset contains raw and processed MEG data for the paper "Model-based aversive learning in humans is supported by preferential task state reactivation" by Toby Wise, Yunzhe Liu, Fatima Chowdhury & Ray Dolan. Raw data is provided as `.fif` files, although it was acquired on a CRF system.

Participants

28

Size

212 GB

Updated

Jun 6, 2026

**ForrestGump-MEG: A audio-visual movie watching MEG dataset** For details please refer to our paper on https://www.biorxiv.org/content/10.1101/2021.06.04.446837v1. This dataset contains MEG data recorded from 11 subjects while watching the 2h long Chinese-dubbed audio-visual movie 'Forrest Gump'. The data were acquired with a 275-channel CTF MEG. Auxiliary data (T1w) as well as derivation data such as preprocessed data and MEG-MRI co-registration are also included. Please noted that For sub

Participants

11

Size

73.5 GB

Updated

Jun 6, 2026

This dataset contains the MEG and structural MRI data from the "Electrophysiological correlates of mood and reward dynamics in human adolescents" pre-registered analysis (https://www.biorxiv.org/content/10.1101/2021.03.04.433969v1). The task-mmi3 data corresponds to the monetary gambling mood induction task described in the paper. Task-mmi3 data has been pre-processed marking bad channels and bad segments (motion > 5mm or/and noise artifacts). Task-rest data is unprocessed 10 minutes resting

Participants

51

Size

123 GB

Updated

Jun 6, 2026

Participants

21

Size

24.5 GB

Updated

Jun 6, 2026

Dataset description: Magnetoencephalography (MEG) dataset recorded during a hMT+ (human visual motion area) localizer task Published in: Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46. Data curation: Sophie Herbst, Alexandre Gramfort This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al.

Participants

11

Size

10.1 GB

Updated

Jun 6, 2026

Stimuli include eight different square wave spiral gratings subtending 10 degrees of visual angle as well as the color words "blue" and "green." The color words appeared as white on a gray background. Each stimulus appeared on the screen for 116 ms. The triggers or event ID's of each stimulus are as follows: 1 - Light Pink Spiral 2 - Dark Pink Spiral 3 - Light Blue Spiral 4 - Dark Blue Spiral 5 - Light Green Spiral 6 - Dark Green Spiral 7 - Light Orange Spiral 8 - Dark Orange Spiral 9 - "green"

Participants

18

Size

214 GB

Updated

Jun 6, 2026

MNE-somato-data-bids ==================== This dataset contains the MNE-somato-data in BIDS format. The conversion can be reproduced through the Python script stored in the `/code` directory of this dataset. See the README in that directory. The `/derivatives` directory contains the outputs of running the FreeSurfer pipeline `recon-all` on the MRI data with no additional commandline options (only defaults were used): $ recon-all -i sub-01\_T1w.nii.gz -s 01 -all After the `recon-all` call, t

Participants

1

Size

334 MB

Updated

Jun 6, 2026

# Brainstorm - Auditory Cortex Mapping Dataset ## License This dataset (MEG and MRI data) was collected by Jonathan Cote of the Neuroplasticity and Sensory Biomarking Lab, Montreal Neurological Institute, McGill University, Canada. Its purpose is to serve as a data example to be used with our MEG-based auditory cortex mapping technique. It is presently released in the Public Domain, and is not subject to copyright in any jurisdiction. We would appreciate though that you reference this d

Participants

1

Size

13.2 GB

Updated

Jun 6, 2026