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nm000263
Visual object ERP EEG dataset (Kaneshiro et al. 2015)

EEG

A high-density 124-channel EEG dataset comprising event-related potentials (ERPs) from 10 healthy participants during a visual object recognition task. Participants viewed 5,184 trials of photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects) with 72 unique images per category, each presented for 500 ms. The dataset is suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.

Participants

10

Channels

124 (egi-geodesic)

HED

v8.4.0

Size

469 MB

Version

v1.0.1

Updated

Jul 10, 2026

by Blair Kaneshiro et al.
nm000262
P300 BCI EEG dataset (Chailloux Peguero et al. 2020)

EEG

A P300 brain-computer interface EEG dataset comprising recordings from 19 healthy participants across 3 sessions. The study investigates the effects of visual stimulation conditions (standard flash versus cartoon face stimuli) on P300-BCI performance using an 8-channel montage sampled at 256 Hz, with data collected using a P300 paradigm across 5 grid sizes and including target and non-target event classifications. This is a BIDS-formatted derivative dataset converted from OpenNeuro ds003190 using the MOABB pipeline.

Participants

19

Channels

8 (10-10)

HED

v8.4.0

Size

463 MB

Version

v1.0.1

Updated

Jul 10, 2026

by J. David Chailloux Peguero et al.
nm000261
Imagined speech EEG dataset — vowels condition (Nguyen et al. 2017)

EEG

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

v1.0.1

Updated

Jul 10, 2026

by Chuong H. Nguyen et al.
nm000258
Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)

EEG

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

v1.0.1

Updated

Jul 10, 2026

by German A. Pressel Coretto et al.
nm000257
Imagined speech EEG dataset — short words condition (Nguyen et al. 2017)

EEG

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

v1.0.1

Updated

Jul 10, 2026

by Chuong H. Nguyen et al.
nm000233
BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery

EEG

BCIComp2020UpperLimb is a preprocessed EEG dataset from BCI Competition 2020 Track 4 containing motor imagery recordings of three upper-limb grasping tasks (cylindrical, spherical, lumbrical) from 15 healthy subjects across three sessions. The dataset comprises 60-channel EEG data sampled at 250 Hz with 450 trials per subject (150 trials per session × 3 sessions), designed to evaluate session-to-session transfer learning in brain-computer interface applications. Data were preprocessed with 60 Hz notch filtering and cue-aligned epoching, with the 4-second motor imagery window extracted for analysis.

Participants

14

Channels

60 (10-10)

HED

v8.4.0

Size

795 MB

Version

v1.0.1

Updated

Jul 10, 2026

by Ji-Hoon Jeong et al.
nm000252
Imagined speech EEG dataset — long words condition (Nguyen et al. 2017)

EEG

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

v1.0.1

Updated

Jul 10, 2026

by Chuong H. Nguyen et al.
nm000220
BEETL Competition 2021 Motor Imagery Dataset A — transfer learning benchmark

EEG

Beetl2021-A is a preprocessed motor imagery EEG dataset from the BEETL Competition Task 2 (NeurIPS 2021), comprising data from 3 healthy subjects collected during an online racing game (Cybathlon2020IC). The dataset contains 63-channel EEG recordings at 500 Hz with four-class motor imagery tasks (rest, left hand, right hand, feet) and serves as a benchmark for evaluating transfer learning and domain adaptation methods across heterogeneous EEG datasets and subjects. This dataset is part of a larger competition focused on advancing transfer learning for subject independence and cross-dataset generalization in brain-computer interfacing.

Participants

3

Channels

63 (10-10)

HED

v8.4.0

Size

216 MB

Version

v1.0.1

Updated

Jul 10, 2026

by [Unspecified1] et al.
nm000224
Imagined speech EEG dataset — short and long words (Nguyen et al. 2017)

EEG

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

v1.0.1

Updated

Jul 10, 2026

by Chuong H. Nguyen et al.
nm000184
BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU)

EEG

This BIDS-formatted derivative dataset comprises EEG recordings from 15 healthy participants performing a visual P300 oddball task during treadmill walking at 1.6 m/s. The study includes simultaneous scalp-EEG (46 channels), ear-EEG (14 channels), electrooculography (4 channels), and inertial measurement unit recordings (6 channels) sampled at 100 Hz. Data were collected as part of BCI Competition 2020 Track 5 to evaluate brain-computer interface performance under ambulatory conditions. Trials are temporally divided into training, validation, and test sets.

Participants

15

Channels

46 (10-10)

HED

v8.4.0

Size

79.7 MB

Version

v1.0.1

Updated

Jul 10, 2026

by Ji-Hoon Jeong et al.