Discover datasets

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

A comprehensive EEG database containing electroencephalographic signals from 87 healthy participants performing motor imagery brain-computer interface tasks. The dataset comprises over 20,800 trials (~70 hours of recording) organized into three datasets (A, B, C) using a standardized Graz protocol for right and left hand motor imagery. In addition to raw EEG signals, the database includes detailed participant demographics, personality profiles, cognitive traits, and BCI performance metrics, enabling research on user-performance relationships, cross-user machine learning algorithms, and profile-informed signal classification.

Participants

87

Channels

27 (10-10)

Citations

38

HED

v8.4.0

Size

15.8 GB

Version

v1.0.4

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 13 healthy subjects performing a visuomotor learning task involving simulated drone piloting through waypoints of varying difficulty levels. The study investigates real-time decoding of subjective difficulty from EEG signals to enable adaptive closed-loop learning, comparing algorithmic difficulty adjustment with subject-controlled progression. Data include 1 offline session and 2 online sessions (online_session_2, online_session_3) with preprocessed EEG recordings (64 channels + 3 EOG, 25 central EEG channels retained after preprocessing) sampled at 256 Hz, along with behavioral markers of task performance including waypoint hits/misses and trajectory events.

Participants

13

Channels

64 (10-10)

Citations

26

HED

v8.4.0

Size

9.42 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 11 ALS patients performing a P300-based brain-computer interface speller task using a 6x6 character grid. The study is part of the BigP3BCI project, the largest public P300 BCI dataset, and includes 16-channel EEG data sampled at 256 Hz with visual stimulus presentations and target/non-target event classifications. Data were collected in a laboratory setting with online feedback to support BCI-based communication applications.

Participants

11

Channels

16 (10-10)

HED

v8.4.0

Size

2.69 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 10 healthy subjects performing a P300-based brain-computer interface task using a 9x8 character grid with face/house visual stimuli. The study is part of the BigP3BCI project, the largest public P300 BCI dataset containing recordings across multiple paradigms and stimulus configurations. Data were acquired at 256 Hz using 32-channel EEG with a g.USBamp amplifier and include target and non-target event classifications for BCI speller applications.

Participants

10

Channels

32 (10-10)

HED

v8.4.0

Size

1.56 GB

Version

v1.0.2

Updated

Aug 18, 2026

A multi-day motor imagery brain-computer interface (MI-BCI) dataset comprising EEG recordings from 51 healthy subjects performing left and right hand motor imagery tasks across three sessions. The dataset includes 59-channel EEG data (plus 1 ECG and 4 EOG channels) sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials with visual and auditory cues. This resource supports the development and benchmarking of BCI algorithms for motor control applications.

Participants

51

Citations

34

HED

v8.4.0

Size

58.4 GB

Version

v1.0.0

Updated

Jul 10, 2026

A motor imagery brain-computer interface dataset comprising EEG recordings from 52 healthy subjects performing kinesthetic imagery of left and right hand finger movements. The dataset includes 100-120 trials per class with concurrent EMG recordings, psychological and physiological questionnaires, 3D electrode coordinates, and non-task-related control states. Mean classification accuracy of 67.46% (±13.17%) demonstrates substantial inter-subject variability in motor imagery discriminability.

Participants

52

Channels

64 (10-10)

Citations

91

HED

v8.4.0

Size

16.7 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises electroencephalographic (EEG) recordings from 71 healthy subjects performing a visual P300-based brain-computer interface task using the Brain Invaders videogame. Participants engaged with an oddball paradigm involving pseudo-random flashes of 36 symbols on a grid to elicit P300 event-related potentials. The study evaluated the feasibility of a calibration-less P300 BCI system using 16 dry electrodes, with real-time adaptive classification for online feedback during gameplay. EEG data were recorded using 16 active dry electrodes with up to three game sessions per subject, comprising 9 levels with up to 8 attempts per level.

Participants

64

Channels

16 (10-10)

Citations

18

HED

v8.4.0

Size

3.06 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset comprises EEG and EMG recordings from 15 healthy participants performing emergency braking tasks during simulated driving at 200 Hz sampling rate. Participants drove a virtual racing car following a lead vehicle, with occasional abrupt decelerations requiring immediate emergency braking responses. The study demonstrates the feasibility of predicting driver braking intentions from neural and muscular signals before behavioral response, with applications to neuroergonomics and driving assistance systems.

Participants

15

Channels

59 (10-10)

Citations

81

HED

v8.4.0

Size

14.2 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains 32-channel EEG recordings from 22 healthy adults performing a visual imagery task involving 10 categories of animals, figures, and objects. Data were collected across two sessions per subject using a Neuracle NeuSenW32 system at 1000 Hz sampling rate, and are formatted according to BIDS for use in brain-computer interface research. The dataset supports evaluation of classification approaches such as CSP and EEGNet for decoding imagined visual categories from EEG signals.

Participants

22

Channels

32 (10-10)

Citations

2

HED

v8.4.0

Size

75.4 GB

Version

v1.0.3

Updated

Aug 20, 2026