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References ---------- Ferrante, O., Liu, L., Minarik, T., Gorska, U., Ghafari, T., Luo, H., & Jensen, O. (2022). FLUX: A pipeline for MEG analysis. NeuroImage, 253, 119047. https://doi.org/10.1016/j.neuroimage.2022.119047 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

Participants

1

Size

3.59 GB

Updated

Jun 6, 2026

This dataset contains the raw MEG data accompanying the paper "The spatiotemporal neural dynamics of object recognition for natural images and line drawings" (Link to preprint: https://biorxiv.org/cgi/content/short/2022.08.12.503484v1). Please cite the above paper if you use this data. The dataset includes: MEG data for 9 runs for each subjects. Events files that contain the onsets, durations and trial types for each trial in the experiment (excluding catch trials). For a full description of

Participants

30

Size

154 GB

Updated

Jun 6, 2026

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., Gramfort,

Participants

30

Size

76.7 GB

Updated

Jun 6, 2026

# Auditory single word recognition in MEG This dataset is described in Gaston et al. (2022). Stimuli and TextGrids are available from the Massive Auditory Lexical Decision database (Tucker et al., 2019). Converted to BIDS using MNE-BIDS (Appelhoff et al., 2019; Niso et al., 2018). # 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.

Participants

18

Size

11.6 GB

Updated

Jun 6, 2026

ILABS amnoise MEG BIDS dataset ============================== This dataset contains MEG data from a single infant subject. For more information, see the following publications, which should be cited if you use this data: - Mittag, M., Larson, E., Clarke, M., Taulu, S., & Kuhl, P. K. (2021). Auditory deficits in infants at risk for dyslexia during a linguistic sensitive period predict future language. NeuroImage: Clinical, 30, 102578. https://doi.org/10.1016/j.nicl.2021.102578 - Mittag, M., Lar

Participants

1

Size

1.78 GB

Updated

Jun 6, 2026

# THINGS-MEG Understanding object representations visual and semantic processing of objects requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. This densely sampled fMRI dataset is part of THINGS-data, a multimodal collection of large-scale datasets comprising functional MRI, magnetoencephalographic recordings, and 4.70 million behavioral judgments in response to thousands of photographic images for up to 1,854 obje

Participants

4

Size

238 GB

Updated

Jun 6, 2026

This data was part of the study of: M.P. Weisend, F.M. Hanlon, R. Montaño, S.P. Ahlfors, A.C. Leuthold, D. Pantazis, J.C. Mosher, A.P. Georgopoulos, M.S. Hämäläinen, C.J. Aine,, V. (2007). Paving the way for cross-site pooling of magnetoencephalography (MEG) data. International Congress Series, Volume 1300, Pages 615-618,. It was converted to BIDS with MNE-BIDS: Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger,

Participants

8

Size

77.2 GB

Updated

Jun 6, 2026

#### Overview This synchronized multimodal neuroimaging dataset for studying brain language processing (SMN4Lang) contains: 1. fMRI and MEG data collected on the same 12 participant while they were listening to 6 hours of naturalistic stories; 2. high-resolution structural (T1, T2), diffusion MRI and resting-state fMRI data for each participant; 3. rich linguistic annotations for the stimuli, including word frequencies, part-of-speech tags, syntactic tree structures, time-aligned characters a

Participants

12

Size

631 GB

Updated

Jun 6, 2026

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

30

Size

78.3 GB

Updated

Jun 6, 2026

### DESCRIPTION Magnetoencephalography (MEG) dataset recorded during the presentation of audiovisual sequences with a causality judgment task and temporal order judgment task. This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al. 2019). ### PUBLISHED IN Pesnot Lerousseau, J., Parise, C., Ernst, MO., van Wassenhove, V. (2022). Multisensory correlation computations in the human brain identified by a time-resolved enc

Participants

13

Size

75.7 GB

Updated

Jun 6, 2026