Discover datasets

A fully anonymized long-term intracranial EEG dataset from 40 patients with pharmacoresistant epilepsy recorded at the Sleep-Wake-Epilepsy-Center, University of Bern. The dataset comprises 5376 hours of continuous iEEG with 381 annotated electrographic seizures, sampled at 512 or 1024 Hz and preprocessed with band-pass filtering and median referencing. This resource supports development and validation of seizure detection and neuronal activity modeling algorithms.

Participants

40

Size

2.96 TB

Version

vv1.0.0

Updated

Jul 10, 2026

This mobile brain body imaging (MoBI) dataset comprises EEG recordings from 18 subjects performing a gait adaptation task on a treadmill. Participants were instructed to synchronize their stepping with an auditory pacing stimulus and adapt their gait parameters (step length and rate) in response to tempo shifts. The dataset provides multimodal neurophysiological data during dynamic motor behavior, enabling investigation of neural mechanisms underlying auditory-motor coupling and gait adaptation.

Participants

20

Channels

108 (10-05)

Size

32.0 GB

Version

vv1.0.0

Updated

Jul 10, 2026

A BIDS-formatted derivative dataset of steady-state visually evoked potential (SSVEP) EEG recordings from 54 subjects across 2 sessions. This dataset comprises 21,600 trials of 4-second EEG recordings at 1000 Hz sampling frequency from 62 channels, organized for standardized brain-computer interface benchmarking. The data has been processed and converted to BIDS format using the Mother of All BCI Benchmarks (MOABB) framework, enabling reproducible analysis of SSVEP-based BCI paradigms with 4 stimulus classes.

Participants

54

Channels

62 (10-05)

Size

16.9 GB

Version

vv1.0.2

Updated

Jul 10, 2026

This dataset comprises preprocessed EEG recordings from 8 healthy subjects performing imagined speech tasks involving three vowel phonemes (a, i, u). Participants received auditory and visual cues to imagine speaking each vowel, with 64-channel EEG data acquired at 256 Hz. The dataset includes 2,400 base trials (8 subjects × 300 trials per subject) with 3 overlapping 2-second epochs extracted per trial, yielding 7,200 total epochs, analyzed using Riemannian manifold-based feature extraction and relevance vector machine classification for brain-computer interface applications. This is a MOABB (Mother of All BCI Benchmarks) benchmark collection dataset converted to BIDS format.

Participants

8

Channels

60 (10-10)

HED

v8.4.0

Size

505 MB

Version

vv1.0.1

Updated

Jul 10, 2026

An open-access EEG dataset containing recordings from 15 healthy Spanish-speaking subjects performing imagined speech tasks. The dataset comprises 11 imagery classes (5 Spanish vowels and 6 directional commands) acquired using 6-channel EEG at 1024 Hz with preprocessed data (bandpass filtered 2-45 Hz). This resource supports brain-computer interface research and motor imagery classification studies.

Participants

15

Channels

6 (10-20)

HED

v8.4.0

Size

362 MB

Version

vv1.0.1

Updated

Jul 10, 2026

This dataset contains preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks with three short word conditions (out, in, up). Data were acquired at 256 Hz using 64 channels and analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications. The motor imagery paradigm employed auditory cueing, yielding 1,800 trials suitable for BCI research and benchmarking.

Participants

6

Channels

60 (10-10)

HED

v8.4.0

Size

379 MB

Version

vv1.0.1

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 6 healthy participants performing imagined speech tasks with two conditions (cooperate and independent word imagery). The study employed a motor imagery paradigm with auditory and visual cues, yielding 1,200 trials that were preprocessed into 3,600 overlapping epochs (1,800 per class) recorded at 256 Hz from 64 channels. Data were analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications, achieving mean classification accuracy of 66.2±4.8%.

Participants

6

Channels

60 (10-10)

HED

v8.4.0

Size

252 MB

Version

vv1.0.1

Updated

Jul 10, 2026

This dataset comprises preprocessed EEG recordings from 6 healthy participants performing imagined speech discrimination tasks between short and long words ('cooperate' vs 'in'). Data were acquired at 256 Hz using 64 EEG channels with standard preprocessing including bandpass filtering (8-70 Hz), notch filtering (60 Hz), and artifact removal. The dataset contains 1,200 trials analyzed using Riemannian manifold and relevance vector machine approaches for brain-computer interface applications, achieving mean classification accuracy of 73.3±8.9%.

Participants

6

Channels

60 (10-10)

HED

v8.4.0

Size

236 MB

Version

vv1.0.1

Updated

Jul 10, 2026

Participants

24

Channels

64

HED

v8.4.0

Size

5.69 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises simultaneous EEG-fMRI recordings from 22 participants (ages 23-51) during naturalistic viewing, collected at the Nathan Kline Institute. The 64-channel EEG data (61 cortical channels, 2 EOG, 1 ECG) was acquired concurrently with fMRI, supplemented by eye-tracking and respiratory recordings. The dataset includes demographic information and behavioral assessments (sleep quality, caffeine intake) to investigate correlations between electrical brain activity and hemodynamic fluctuations during naturalistic stimulation.

Participants

22

Channels

61 (10-10)

Citations

40

Size

256 GB

Version

vv1.0.0

Updated

Jul 10, 2026