Discover datasets

This dataset comprises EEG recordings from 10 patients with spinal cord injury performing five distinct hand and forearm motor imagery tasks: hand open, palmar grasp, lateral grasp, pronation, and supination. The study investigates the decoding of attempted movements from low-frequency EEG signals (movement-related cortical potentials) to support neuroprosthetic applications for upper limb control. Data include 360 trials (72 per class) acquired at 256 Hz with 61 EEG channels and 3 EOG channels, preprocessed with ICA-based artifact rejection and bandpass filtering.

Participants

10

Channels

61 (10-05)

Citations

97

HED

v8.4.0

Size

4.51 GB

Version

v1.0.2

Updated

Aug 18, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005935-blue…

Participants

21

Size

739 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings from 50 acute stroke patients (1-30 days post-stroke) performing motor imagery tasks of left- and right-handed hand-grip movements. Recorded using a wireless 29-channel EEG system at 500 Hz, the dataset includes raw and preprocessed data, representing the first open resource addressing left- and right-handed motor imagery in the acute stroke population. The dataset supports brain-computer interface (BCI) algorithm development and clinical rehabilitation applications.

Participants

50

Channels

29 (10-10)

Citations

78

HED

v8.4.0

Size

1.15 GB

Version

v1.0.3

Updated

Aug 18, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006903-blue…

Participants

17

Size

5.43 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains EEG recordings from 127 young adults (18-30 years) collected alongside measures of childhood and adulthood socioeconomic status (SES), including educational attainment, income, food security, and neighborhood characteristics. EEG tasks were drawn from or adapted from the ERP CORE resource, designed to elicit neural activity related to perception, cognition, and action. The dataset also includes an ADHD symptoms checklist, enabling investigation of relationships between SES, ADHD symptoms, and neural activity in a socioeconomically diverse adult sample.

Participants

127

Channels

27

Citations

7

Size

10.6 GB

Version

v1.0.0

Updated

Aug 19, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005776-blue…

Participants

11

Size

1.20 GB

Version

v1.0.0

Updated

Jul 10, 2026

The BNCI 2014-001 Motor Imagery dataset is a widely-used benchmark for brain-computer interface research, comprising EEG recordings from 9 healthy subjects performing four-class motor imagery tasks (left hand, right hand, feet, and tongue). Each subject completed two sessions with 6 runs per session, yielding 200 training and 240 test trials. The dataset features 22 EEG channels plus 3 EOG channels (25 total) sampled at 250 Hz with minimal preprocessing (bandpass filtering 0.05-200 Hz), making it a standard resource for evaluating multi-class motor imagery classification algorithms and cross-session transfer learning approaches.

Participants

9

Channels

22 (10-10)

Citations

90

HED

v8.4.0

Size

1.38 GB

Version

v1.0.2

Updated

Aug 18, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003039-blue…

Participants

19

Channels

64 (10-05)

Citations

22

Size

7.82 GB

Version

v1.0.0

Updated

Jul 10, 2026

Participants

4

Channels

14 (10-10)

Citations

82

Size

152 MB

Version

v1.0.0

Updated

Jul 10, 2026

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006673-blue…

Participants

17

Size

20.7 GB

Version

v1.0.0

Updated

Jul 10, 2026