Discover datasets

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

This dataset comprises electroencephalography recordings from 16 subjects viewing rapid serial visual presentations of gabor-like stimuli at two presentation rates (6.67 Hz and 20 Hz). Participants performed a concurrent fixation color change detection task while EEG data were acquired. The study investigates the dynamics of visual feature coding and neural mechanisms underlying perception and feature integration.

Participants

16

Citations

27

Size

69.6 GB

Version

v1.0.0

Updated

Jul 10, 2026

A classical motor imagery EEG dataset comprising recordings from 7 healthy subjects performing left hand, right hand, and passive rest tasks. The dataset contains 19-channel EEG data sampled at 200 Hz with visual arrow cues guiding motor imagery tasks. This benchmark dataset is designed for evaluating brain-computer interface algorithms and motor imagery classification methods.

Participants

7

Channels

19 (10-20)

Citations

81

HED

v8.4.0

Size

1.20 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 84 healthy participants performing motor imagery tasks across multiple paradigms (Graz motor imagery, SSMVEP-MI, and hybrid video/SSVideo paradigms) in two distinct recording environments: a controlled electromagnetically shielded laboratory and a simulated multi-sensory hospital setting. A supplementary dataset from 3 participants recorded in a space station environment is also included, extending the study to microgravity conditions. The dataset is intended to support research on cross-environment robustness, cross-subject decoding, and benchmarking of EEG-based brain-computer interface algorithms.

Participants

84

Channels

69 (10-10)

Citations

2

Size

144 GB

Version

v1.0.0

Updated

Aug 18, 2026

This dataset contains EEG recordings from 12 healthy human subjects performing three-class motor imagery tasks involving the same upper extremity: rest, imagined grasping, and imagined elbow flexion. Data were collected across four sessions per subject using a 32-channel EEG system, and the dataset is a BIDS-formatted derivative of the original study on classifying imaginary motor states using time-domain features. The dataset supports research in brain-computer interfaces (BCI) for motor rehabilitation applications.

Participants

12

Channels

32 (egi-geodesic)

Citations

44

HED

v8.4.0

Size

52.0 GB

Version

v1.0.3

Updated

Aug 20, 2026