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NOD-MEG provides magnetoencephalography (MEG) recordings from human participants viewing large-scale naturalistic ImageNet stimuli, extending the previously collected Natural Object Dataset (NOD-fMRI) with temporally resolved neural data. Combined with corresponding fMRI and EEG datasets from the same subjects, NOD-MEG enables multimodal investigation of object recognition across both spatial and temporal domains. The dataset is intended as a resource for studying neural mechanisms of visual object recognition under naturalistic viewing conditions.

Participants

31

Citations

1

Size

215 GB

Version

v1.0.0

Updated

Aug 19, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005486-blue…

Participants

159

Citations

6

Size

371 GB

Version

v1.0.0

Updated

Aug 19, 2026

NOD-EEG provides electroencephalography (EEG) recordings collected from the same subjects and using the same naturalistic ImageNet stimuli as the previously published NOD-fMRI dataset, extending the Natural Object Dataset (NOD) to include temporal dynamics of visual object recognition. Combined with companion MEG data, this multimodal resource enables investigation of neural mechanisms underlying object recognition across naturalistic scenes with both high spatial and high temporal resolution. The dataset includes raw and preprocessed EEG time series and epoched data, along with detailed trial-level event metadata.

Participants

19

Channels

62 (10-10)

Citations

7

Size

24.3 GB

Version

v1.0.0

Updated

Aug 19, 2026

This magnetoencephalography (MEG) dataset investigates the differential neural mechanisms underlying selection and maintenance of information during working memory tasks. Data from 22 participants include MEG recordings across two sessions, structural MRI, and detailed behavioral measures from a working memory task and a one-back control task involving visual Gabor stimuli. The dataset supports investigation of how the brain selectively maintains task-relevant information while filtering distractions.

Participants

22

Size

168 GB

Version

v1.0.0

Updated

Jul 10, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006104-blue…

Participants

24

Channels

61 (10-10)

Citations

6

Size

83.8 GB

Version

v1.0.0

Updated

Aug 19, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006374-blue…

Participants

36

Channels

32 (10-10)

Size

42.7 GB

Version

v1.0.0

Updated

Aug 19, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005307-blue…

Participants

7

Channels

72 (other)

Size

18.6 GB

Version

v1.0.0

Updated

Aug 19, 2026

VEPCON is a multimodal neuroimaging dataset comprising high-density EEG, structural MRI, and diffusion-weighted imaging from 20 participants performing visual discrimination tasks (face vs. scrambled faces, coherent vs. incoherent motion). The dataset includes preprocessed EEG single trials, individual brain parcellations at five spatial resolutions, structural connectomes derived from diffusion data, and EEG source imaging solutions based on individual anatomy. This resource supports multimodal methods development, structure-function relationship studies, and optimization of source imaging and graph analysis techniques.

Participants

20

Channels

128 (biosemi)

Citations

24

Size

58.2 GB

Version

v1.0.0

Updated

Jul 22, 2026

…Simultaneous tDUS and blinking light increased cortical activity compared to light alone…

Participants

20

Size

8.42 GB

Version

v1.0.0

Updated

Aug 19, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005931-blue…

Participants

8

Size

818 MB

Version

v1.0.0

Updated

Aug 19, 2026