Discover datasets

A visual event-related potential dataset of 124-channel EEG recorded from 10 healthy participants viewing photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, inanimate object). It contains 5,184 trials, with 72 images per category shown for 500 ms with a 750 ms interstimulus interval. The data support research on neural object representations through single-trial EEG classification and representational similarity analysis, and are distributed in BIDS format via MOABB.

Participants

10

Channels

124 (egi-geodesic)

Citations

93

HED

v8.4.0

Size

3.40 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset contains raw neuroimaging recordings collected with the Visual Attribute-Specific Contextual Trajectory Paradigm 2.0. The paradigm is used to study how the brain generates domain-general visual surprise signals and how their latency depends on the visual attribute that is violated. It accompanies a pre-print on attribute-dependent latency of visual surprise signals.

Participants

35

Citations

1

Size

11.4 GB

Version

v1.0.0

Updated

Jun 29, 2026

This dataset provides simultaneous EEG and fNIRS recordings from 12 participants performing a silent naming task and three sensory-based imagery tasks (visual, auditory, tactile) on objects from the semantic categories of animals and tools. It supports research on semantic decoding and brain-computer interfacing. Stimulus images and example Python scripts for loading the data with MNE and MNE-BIDS are included.

Participants

12

Channels

64 (biosemi)

Citations

11

Size

24.2 GB

Version

v1.0.0

Updated

Jun 24, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003690-blue…

Participants

75

Channels

61 (10-10)

Citations

30

Size

21.5 GB

Version

v1.0.0

Updated

Jun 22, 2026

…Parallel models might make the perceptual system more robust, facilitate processing of…

Participants

13

Citations

80

Size

41.6 GB

Version

v1.0.0

Updated

Sep 8, 2026

THINGS-EEG2 is a large EEG dataset from 10 participants who viewed 16,540 training images and 200 repeatedly presented test images from the THINGS database. Images were shown in rapid serial visual presentation at 5 Hz while 63-channel EEG was recorded at 1000 Hz across four sessions per subject. The dataset supports modeling of human visual object recognition and includes resting-state recordings, the authors' preprocessed epochs, and stimulus images.

Participants

10

Channels

63 (10-10)

Citations

88

Size

241 GB

Version

v1.1.0

Updated

Jul 5, 2026

Scalp EEG recorded from 45 participants listening to a 12.4-minute audiobook recording of the first chapter of Alice's Adventures in Wonderland. The naturalistic paradigm was designed to study how the brain builds hierarchical syntactic structure and makes rapid, incremental linguistic predictions. The dataset includes word-level events annotated with lexical frequency, sound power, and n-gram, RNN, and context-free-grammar surprisal predictors, along with comprehension scores and the audio stimuli.

Participants

45

Channels

60 (other)

Citations

91

Size

3.88 GB

Version

v1.1.3

Updated

Jul 3, 2026

EEG recordings from 13 healthy participants performing a copy-spelling task with a visual 6x7 matrix ERP speller. The speller was designed for learning from label proportions (LLP), an unsupervised, calibrationless classification approach for ERP-based brain-computer interfaces. Two stimulus sequences with different target/non-target ratios allowed LLP to reconstruct mean target and non-target responses without labeled data. In the original online study, 84.5% of characters were spelled correctly on average without prior calibration.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.3

Updated

Oct 8, 2026

…Furthermore, our investigation focuses on reducing the perceptual saliency of c-VEP…

Participants

12

Channels

32 (10-10)

Citations

18

HED

v8.4.0

Size

337 MB

Version

v1.0.3

Updated

Oct 8, 2026

This dataset contains 64-channel EEG recordings from people with and without vicarious touch experiences. Participants either felt touch to their fingers or watched matched videos of touch to another person's fingers. It was collected to test, using time-resolved multivariate pattern analysis, whether seen touch evokes neural representations that overlap with first-hand touch. Raw and processed data, code and stimuli are included.

Participants

40

Channels

64

Citations

12

Size

172 GB

Version

v1.0.0

Updated

Jun 24, 2026