Discover datasets

Brain Treebank is a large-scale intracranial EEG dataset comprising 43 hours of iEEG recordings from 10 epilepsy patients watching naturalistic Hollywood movies, with 1,688 electrodes sampled at 2048 Hz. The dataset includes time-aligned linguistic annotations with word-level transcripts and Universal Dependencies syntax trees, providing a unique resource for studying neural language processing during naturalistic stimulation.

Participants

10

Size

508 GB

Version

v1.0.0

Updated

Jul 10, 2026

A synchronized multimodal neuroimaging dataset containing concurrent fMRI and MEG recordings from 12 Mandarin Chinese speakers during naturalistic story listening, supplemented with high-resolution structural imaging, diffusion MRI, and resting-state fMRI. The dataset includes rich linguistic annotations of stimuli encompassing word frequencies, syntactic structures, temporal alignments, and embeddings from multiple pre-trained language models, enabling comprehensive investigation of neural mechanisms underlying language processing.

Participants

12

Citations

6

Size

1.36 TB

Version

v1.0.0

Updated

Jul 22, 2026

This MEG dataset comprises auditory cortex mapping data from a single subject acquired using a CTF 275 system at 120 kHz sampling rate. The experiment involved passive listening to 1,795 iso-intensity pure tones ranging from 100 Hz to 21.5 kHz presented binaurally over a 12-minute acquisition run. The dataset includes anatomical MRI data processed with FreeSurfer and serves as a reference example for MEG-based auditory cortex mapping techniques.

Participants

2

Citations

2

Size

15.5 GB

Version

v1.0.0

Updated

Jul 10, 2026

2417 clinical EEG recordings, 19-ch 10-20 montage, normal/abnormal labels. EDF format. Zenodo DOI:10.5281/zenodo.10909103

Participants

2417

Channels

21 (10-20)

Size

13.8 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains EEG recordings from 34 participants collected to investigate the adaptive recruitment of cortex-wide recurrent processing during visual object recognition. Participants viewed a stimulus set of 242 images, comprising 'challenge' and 'control' images selected based on discrepancies between human behavioral performance and AlexNet classification, while performing a rapid serial visual presentation task with a paper-clip detection component. The dataset includes derivatives with time-resolved decoding accuracy matrices for object identity, supporting analyses of recurrent cortical dynamics in visual processing.

Participants

34

Channels

63 (10-10)

Citations

1

Size

61.2 GB

Version

v1.0.0

Updated

Aug 19, 2026

NeuroMorph is a high-temporal resolution MEG dataset designed for morpheme-based linguistic analysis, comprising recordings from 24 subjects totaling over 17 hours of data. Data were collected using a KIT/Yokogawa MEG system to investigate visual language and cognitive processing, with a focus on morphological structure in written language comprehension.

Participants

24

Size

140 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains intracranial electrophysiological recordings from the auditory cortex of macaque monkeys, collected to investigate neural responses in the auditory system. The data were acquired by Kajikawa and Schroeder and are mirrored on NEMAR from OpenNeuro under the DISC data collection.

Participants

3

Citations

3

Size

13.9 GB

Version

v1.0.0

Updated

Aug 19, 2026

FLUX is a comprehensive pipeline for magnetoencephalography (MEG) data analysis, providing standardized workflows for processing and analyzing MEG recordings. This dataset demonstrates the application of the FLUX pipeline to MEG neuroimaging data organized according to the Brain Imaging Data Structure (BIDS) standard, facilitating reproducible and transparent MEG analysis across research groups. The dataset is derived from OpenNeuro ds004346 and contains MEG recordings with anatomical data.

Participants

1

Citations

41

Size

16.8 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.2

Updated

Aug 18, 2026

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