Discover datasets

This dataset comprises preprocessed electroencephalography (EEG) recordings from 20 healthy participants performing a motor imagery task involving discrete reaching movements in four directions (up, down, left, right) with varying speeds and distances. Participants executed 960 trials across 10 blocks while viewing visual cues, with concurrent eye-tracking and motion capture data. The dataset includes extensive preprocessing with artifact correction, source localization, and classification features, making it suitable for brain-computer interface research and motor imagery decoding studies.

Participants

20

Channels

67 (10-05)

Citations

3

HED

v8.4.0

Size

15.4 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains EEG recordings from Experiment 2 of a study investigating memory reactivation under anticipated interference, using a working memory task with lateralized visual objects and dual-task distractor manipulations. Participants memorized cued objects, performed a secondary distractor identification task in some blocks, and were subsequently tested on their memory via a two-alternative forced-choice probe. The dataset is intended to examine how anticipated interference affects neural memory reactivation processes.

Participants

56

Channels

30 (10-10)

Size

40.1 GB

Version

v1.0.0

Updated

Aug 19, 2026

This EEG dataset investigates how diffuse (categorical) predictions about stimulus orientation shape neural representations during memory encoding. Participants performed an orientation working-memory task in which a central color cue indicated whether an upcoming grating's orientation was drawn from a predictable 90° range or was fully unpredictable, and later judged whether a probe was rotated clockwise or counterclockwise relative to the memorized orientation. The study examines how predictive information stabilizes and reshapes neural codes for encoded visual features.

Participants

47

Channels

66 (10-10)

Size

145 GB

Version

v1.0.0

Updated

Aug 18, 2026

This dataset contains 64-channel EEG recordings from 36 participants performing a visual tilt illusion task, in which the perceived orientation of a central grating was biased by a surrounding annular grating. The study examines how prestimulus alpha oscillation power and phase relate to discrimination performance and the magnitude of the tilt illusion. Data were collected to support investigations into the neural correlates of perceptual bias in orientation discrimination.

Participants

36

Channels

64

Citations

1

Size

83.2 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises high-density, dual-layer EEG, neck EMG, IMU acceleration, ground reaction force, and structural MRI-derived head model data from 32 young adult participants walking over uneven terrain and at varying speeds. Each participant completed two three-minute trials per condition along with a seated rest trial. The dataset is designed to support research on the neural and biomechanical correlates of walking balance and gait adaptation on irregular surfaces.

Participants

32

Channels

120 (other)

Citations

3

Size

62.5 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains EEG recordings from two experiments investigating the cortical encoding of sound source elevation in humans. Subjects were presented with noise bursts from loudspeakers positioned at various elevations in a hemi-anechoic chamber, and were also tested on their ability to localize elevated sound sources. Experiment I used a deviant detection paradigm, while Experiment II used a one-back task involving sound elevation discrimination.

Participants

53

Channels

64 (other)

Size

18.2 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

A gaze-independent brain-computer interface dataset based on covert spatial attention shifts for binary communication. This EEG dataset comprises recordings from 18 healthy participants performing a visual attention task with N2pc event-related potential detection. The paradigm uses colored visual stimuli (green crosses for 'yes', red crosses for 'no') presented while maintaining central gaze fixation, achieving 88.5% mean accuracy with canonical correlation analysis classification.

Participants

18

Channels

30 (10-10)

Citations

26

HED

v8.4.0

Size

1.82 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from participants performing go-nogo categorization and detection tasks across two recording sessions. Participants viewed briefly presented images (20 ms) and responded according to task-specific rules: lifting a finger for target stimuli (go response) or maintaining button press for non-targets (nogo response). The dataset includes two complementary tasks—animal categorization and image recognition—with systematic manipulation of stimulus presentation and response requirements, providing a rich resource for investigating event-related potentials and decision-making processes.

Participants

14

Channels

31 (10-20)

Size

9.22 GB

Version

v1.0.0

Updated

Jul 10, 2026

This high-density functional near-infrared spectroscopy (fNIRS) dataset comprises neuroimaging recordings from the motor cortex during three experimental paradigms: resting state, ball-squeezing motor tasks (both hands performed sequentially), and purposeful motion artifact creation. The dataset includes concurrent accelerometer measurements and is designed to support the development and validation of motion artifact correction algorithms in fNIRS neuroimaging.

Participants

12

Size

572 MB

Version

v1.0.0

Updated

Jul 10, 2026