Discover datasets

This dataset comprises scalp EEG recordings from 27 stroke patients performing a lower-limb motor imagery task as part of a multi-paradigm, longitudinal rehabilitation training protocol. Data were collected at Tianjin University to support research on motor-imagery brain-computer interfaces for gait and lower-limb rehabilitation after stroke. The dataset is organized in BIDS format with a single EEG task (task-imagery) across repeated sessions.

Participants

27

Channels

64 (10-10)

Citations

23

HED

v8.4.0

Size

56.7 GB

Version

v1.0.5

Updated

Aug 20, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005777-blue…

Participants

14

Size

865 MB

Version

v1.0.0

Updated

Jul 10, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on007719-blue…

Participants

65

Size

8.63 GB

Version

v1.0.0

Updated

Jul 10, 2026

Beetl2021-A is a preprocessed motor imagery EEG dataset derived from the BEETL Competition 2021 (NeurIPS Task 2), comprising 63-channel, 500 Hz recordings from healthy subjects performing a four-class motor imagery task (rest, left hand, right hand, feet) during an online BCI racing game (Cybathlon2020IC). The dataset was designed to benchmark transfer learning and domain adaptation methods for subject-independent, cross-dataset EEG-based brain-computer interfacing.

Participants

3

Channels

63 (10-10)

Citations

2

HED

v8.4.0

Size

2.17 GB

Version

v1.0.6

Updated

Aug 20, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004851-blue…

Participants

66

Channels

72 (10-10)

Citations

2

Size

56.6 GB

Version

v1.0.0

Updated

Jul 10, 2026

Imported from OpenNeuro ds004022

Participants

7

Channels

18

Citations

2

Size

635 MB

Version

v1.0.0

Updated

Jul 10, 2026

…Multimodal datasets of brain data enable the fusion of neuroimaging modalities with…

Participants

4

Channels

64 (10-10)

Citations

27

Size

9.33 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises longitudinal motor imagery EEG recordings from 18 BCI-naive subjects across six sessions (one offline, five online), designed to study transfer learning and skill acquisition in brain-computer interfaces (BCIs). It compares two domain adaptation frameworks—Generic Recentering and Personally Assisted Recentering—for calibration-free BCI training using left/right hand motor imagery with visual feedback. Data were collected at 512 Hz with 22 EEG channels and analyzed using Riemannian geometry-based classifiers.

Participants

18

Channels

22 (10-10)

Citations

49

HED

v8.4.0

Size

14.4 GB

Version

v1.0.4

Updated

Aug 20, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005929-blue…

Participants

7

Size

68.5 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 15 healthy participants performing reach-and-grasp motor imagery tasks using three different electrode systems: gel-based laboratory equipment, water-based mobile EEG, and dry-electrode mobile EEG. Data were acquired at 256 Hz from 58 EEG channels plus 6 EOG channels across three sessions with 7200 total trials. Participants executed palmar grasp (toward glass) and lateral grasp (toward spoon) actions. The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.

Participants

45

Channels

58 (10-05)

Citations

51

HED

v8.4.0

Size

8.67 GB

Version

v1.0.3

Updated

Aug 18, 2026