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This magnetoencephalography (MEG) dataset investigates neural object representations during dynamic visual occlusion. Participants viewed objects that were either occluded or disappeared while their neural activity and eye movements were recorded. The dataset includes raw MEG data, behavioral responses, eye-tracking recordings, and preprocessed neural signals epoched relative to stimulus onset and position changes, enabling investigation of how the brain maintains object representations under conditions of visual disruption.

Participants

27

Size

258 GB

Version

v1.0.0

Updated

Jul 10, 2026

The NIMH Healthy Research Volunteer Dataset is a comprehensive neuroimaging and clinical characterization study of 1,859 healthy adults. The dataset includes structural and functional MRI, diffusion tensor imaging, magnetoencephalography, cognitive assessments, psychiatric evaluations, and biospecimens. This resource enables investigations into normal cognition, mood regulation, and brain function across a deeply phenotyped healthy population.

Participants

251

Citations

29

Size

633 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises magnetoencephalography (MEG) recordings from 50 healthy French-speaking adults reading the French text of *Le Petit Prince* presented via rapid serial visual presentation (RSVP). Each participant completed 9 experimental runs while MEG, electrooculogram, and electrocardiogram signals were continuously recorded at 1000 Hz. High-resolution anatomical MRI scans were acquired for each participant to enable source localization and cortical surface reconstruction.

Participants

50

Citations

29

Size

299 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises magnetoencephalography (MEG) recordings from 58 healthy French-speaking adults listening to the French audiobook of *Le Petit Prince*. Participants completed 9 runs of continuous listening while MEG signals were recorded alongside electrooculography and electrocardiography. High-resolution anatomical MRI scans were acquired for each participant to enable source localization and cortical surface reconstruction, supporting investigations of neural language processing during naturalistic speech comprehension.

Participants

58

Citations

29

Size

445 GB

Version

v1.0.0

Updated

Jul 10, 2026

This MEG dataset contains recordings from a cognitive neuroscience study investigating planning, memory, and replay mechanisms. The dataset includes magnetoencephalography recordings with synchronized trigger channels for precise event marking, along with detailed event timing information. Participants completed multiple experimental phases including localizer, structure and reward learning, and post-task sessions. The data is provided in BIDS format with comprehensive documentation of trigger timing and photodiode-estimated visual onset delays.

Participants

1

Citations

2

Size

1.28 GB

Version

v1.0.0

Updated

Jul 10, 2026

Imported from OpenNeuro ds005261

Participants

17

Citations

5

Size

225 GB

Version

v1.0.0

Updated

Jul 10, 2026

MEG-MASC is a high-quality magnetoencephalography dataset comprising raw MEG recordings from 27 English speakers listening to approximately two hours of naturalistic stories from the Manually Annotated Sub-Corpus (MASC). The dataset includes precise temporal annotations of word and phoneme onsets/offsets, organized according to the Brain Imaging Data Structure (BIDS) standard. This benchmark dataset enables large-scale encoding and decoding analyses of neural responses to natural speech processing, with accompanying code for validation analyses including temporal decoding of phonetic features and word frequency effects.

Participants

27

Citations

46

Size

99.3 GB

Version

v1.0.1

Updated

Jul 10, 2026

This dataset comprises multi-subject, multimodal neuroimaging data (structural MRI, fMRI, MEG, and EEG) collected during a face processing task, in which participants viewed famous, unfamiliar, and scrambled faces presented under initial, immediate repeat, and delayed repeat conditions. It is a BIDS-formatted subset of the original Wakeman & Henson (2015) dataset, designed to support research on multimodal integration of brain imaging data and studies of face recognition and repetition effects.

Participants

17

Citations

83

Size

169 GB

Version

v1.0.0

Updated

Aug 18, 2026