Discover datasets

This dataset comprises multi-channel EEG recordings from 27 healthy adults performing a sustained-attention driving task in a virtual-reality simulator across 62 sessions. The task involved event-related lane-departure paradigms where participants maintained vehicle position on a simulated highway, with recordings capturing 32-channel EEG (30 scalp + 2 mastoid references) sampled at 500 Hz alongside vehicle position data. The dataset includes approximately 82 hours of raw, unfiltered EEG data with over 27,000 lane-departure trials and associated behavioral markers, providing a resource for investigating fatigue, drowsiness, and sustained attention mechanisms.

Participants

27

Channels

32 (10-10)

HED

v8.2.0

Size

36.4 GB

Version

vv1.0.0

Updated

Jul 10, 2026

Beetl2021-B is a preprocessed 32-channel EEG dataset from the NeurIPS 2021 BEETL competition designed for benchmarking transfer learning and domain adaptation algorithms in motor imagery brain-computer interfaces. The dataset comprises EEG recordings from 2 healthy subjects performing 4-class motor imagery tasks (left hand, right hand, feet, and rest) sampled at 200 Hz, with 1590 total trials organized for cross-subject and cross-dataset generalization assessment.

Participants

2

Channels

32 (10-10)

HED

v8.4.0

Size

31.3 MB

Updated

Jul 10, 2026

BigP3BCI Study A is a derivative P300 brain-computer interface dataset containing 32-channel EEG recordings from 13 healthy subjects performing a visual speller task using a 6×6 checkerboard stimulus paradigm. The dataset comprises EEG data sampled at 256 Hz with binary classification of target and non-target events, annotated using HED 8.4.0 schema. This represents Study A of the largest public P300 BCI dataset and has been processed using the MOABB (Mother of All BCI Benchmarks) framework.

Participants

13

Channels

32 (10-10)

HED

v8.4.0

Size

868 MB

Version

vv1.0.1

Updated

Jul 10, 2026

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

vv1.0.1

Updated

Jul 10, 2026

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

vv1.0.1

Updated

Jul 10, 2026

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

vv1.0.1

Updated

Jul 10, 2026

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

vv1.0.1

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 15 healthy participants performing self-initiated reach-and-grasp motor tasks using three different recording 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 a total of 7,200 trials. Participants executed palmar and lateral grasp actions toward objects while EEG signals were recorded. 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)

HED

v8.4.0

Size

2.08 GB

Version

vv1.0.1

Updated

Jul 10, 2026

A longitudinal motor imagery EEG dataset from 18 brain-computer interface (BCI)-naive subjects acquired across 6 sessions (1 offline + 5 online) to investigate transfer learning and domain adaptation for calibration-free BCI training. Subjects performed left/right hand motor imagery tasks with visual feedback using 22 EEG channels sampled at 512 Hz. The dataset compares Generic Recentering (unsupervised) and Personally Assisted Recentering (supervised) domain adaptation frameworks, with features extracted as covariance matrices and classified using Riemannian geometry-based methods.

Participants

18

Channels

22 (10-10)

HED

v8.4.0

Size

1.66 GB

Version

vv1.0.1

Updated

Jul 10, 2026

Participants

19

Channels

16 (10-10)

HED

v8.4.0

Size

1.20 GB

Version

vv1.0.0

Updated

Jul 10, 2026