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Beetl2021-B is a preprocessed EEG dataset from the NeurIPS 2021 BEETL competition, focused on transfer learning for motor imagery decoding. It contains 32-channel EEG recordings from 2 healthy subjects performing a 4-class motor imagery task (left hand, right hand, feet, rest) sampled at 200 Hz. The dataset is designed to benchmark transfer learning and domain adaptation algorithms addressing cross-subject and cross-dataset generalization challenges in brain-computer interfaces.

Participants

2

Channels

32 (10-10)

Citations

2

HED

v8.4.0

Size

1.99 GB

Version

v1.0.5

Updated

Aug 20, 2026

An EEG dataset of imagined speech from 15 healthy participants comparing traditional cue-based and gamified paradigm designs for brain-computer interface applications. The dataset comprises 1,800 trials of four Spanish directional commands recorded at 500 Hz from 24 electrodes using the mBrainTrain Smarting system. This derivative dataset enables systematic evaluation of paradigm design effects on imagined speech decoding performance.

Participants

15

Channels

24 (10-10)

Citations

1

HED

v8.4.0

Size

1.52 GB

Version

v1.0.3

Updated

Aug 18, 2026

Imported from OpenNeuro ds005028

Participants

11

Citations

2

Size

1.46 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) signals acquired from 10 naive participants using a low-cost consumer-grade EEG device during a motor imagery brain-computer interface protocol. Participants performed kinesthetic imagination of dominant-hand grasping movements and rest conditions across five protocol runs, with electromyography (EMG) recorded from the dominant hand for protocol validation. EEG signals were bandpass-filtered between 0.5 and 45 Hz using a 3rd-order Butterworth filter at a sampling rate of 125 Hz.

Participants

10

Channels

15 (10-20)

Citations

66

Size

69.3 MB

Version

v1.0.2

Updated

Jul 10, 2026

This dataset comprises multi-subject, multi-modal neuroimaging recordings (structural MRI, MEG, and EEG) collected during a median nerve stimulation paradigm. Participants received electrical stimuli to the right wrist median nerve and responded by lifting their left index finger as quickly as possible. The dataset supports investigation of somatosensory and motor cortical responses using combined MEG/EEG/MRI methodology.

Participants

5

Size

3.07 GB

Version

v1.0.0

Updated

Aug 19, 2026

BIDS-EMG dataset from Grison et al. 2025 - HDsEMG recordings of tibialis anterior during isometric contractions at 10-70% MVC, 1 subject, 128-channel grids (2x64), with concurrent intramuscular EMG ground truth labels (MUniverse benchmark)

Participants

1

Size

2.68 GB

Version

v1.0.1

Updated

Jul 10, 2026

This dataset contains EEG recordings from 10 healthy adults performing a P300 speller task using a 6x6 character matrix, under two stimulus conditions (Famous Faces overlay and Inverting). Data were collected across two sessions per subject with three runs each, using a 32-channel g.tec EEG system at 256 Hz. The dataset is a BIDS-formatted derivative generated via MOABB from the original data reported in Speier et al. (2017), which compared classification approaches for the P300 speller using language models.

Participants

10

Channels

32 (10-10)

Citations

28

HED

v8.4.0

Size

773 MB

Version

v1.0.4

Updated

Aug 20, 2026