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This dataset comprises electroencephalographic recordings from 24 healthy subjects performing a visual P300 brain-computer interface task based on the Brain Invaders video game paradigm. The study compares adaptive versus non-adaptive calibration approaches using Riemannian geometry for classifier training. EEG data were acquired using 16 electrodes at 512 Hz sampling rate during sessions conducted at GIPSA-lab, Grenoble, France in 2013.

Participants

24

Channels

16 (10-10)

Citations

6

HED

v8.4.0

Size

1.71 GB

Version

vv1.0.1

Updated

Jul 10, 2026

This dataset comprises electroencephalographic recordings from 25 healthy subjects performing a visual P300 brain-computer interface task based on the Brain Invaders paradigm. Participants viewed flashing alien groups and silently counted target stimuli while 16-channel EEG was recorded at 128 Hz. The dataset includes both training and online sessions with performance feedback, providing a benchmark resource for P300-based BCI research and algorithm validation.

Participants

23

Channels

17 (10-20)

Citations

3

HED

v8.4.0

Size

163 MB

Version

vv1.0.1

Updated

Jul 10, 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 investigations of user-profile relationships with BCI performance and development of cross-user machine learning algorithms.

Participants

87

Channels

27 (10-10)

Citations

35

HED

v8.4.0

Size

8.83 GB

Version

vv1.0.2

Updated

Jul 10, 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 offline and online sessions with preprocessed EEG recordings sampled at 256 Hz. Original 64-channel recordings were preprocessed and reduced to 25 central channels (peripheral electrodes removed) for analysis, along with behavioral markers of task performance.

Participants

13

Citations

22

HED

v8.4.0

Size

2.96 GB

Version

vv1.0.0

Updated

Jul 10, 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 acquired using g.USBamp hardware and annotated with HED 8.4.0 event tags for standardized analysis.

Participants

11

HED

v8.4.0

Size

770 MB

Version

vv1.0.0

Updated

Jul 10, 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, and includes single-session recordings with 32-channel EEG data sampled at 256 Hz. Data were collected for speller application development with online feedback in a laboratory setting.

Participants

10

HED

v8.4.0

Size

474 MB

Version

vv1.0.0

Updated

Jul 10, 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

29

HED

v8.4.0

Size

58.4 GB

Version

vv1.0.0

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 performance variation across subjects, making it valuable for investigating subject-to-subject transfer in BCI applications.

Participants

52

Citations

92

HED

v8.4.0

Size

6.75 GB

Version

vv1.0.0

Updated

Jul 10, 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. Subjects completed up to three game sessions.

Participants

64

Citations

18

HED

v8.4.0

Size

1.03 GB

Version

vv1.0.0

Updated

Jul 10, 2026

A 7-day longitudinal motor imagery brain-computer interface (BCI) EEG dataset comprising 20 healthy participants performing four imagery tasks (left hand, right hand, both feet, and rest) across 7 sessions. The dataset contains 33,600 trials recorded at 500 Hz from 41 EEG channels using a Neuroscan SynAmps2 system with visual arrow cue stimulation. Data were preprocessed and annotated with HED 8.4.0 tags, supporting BCI research and algorithm development for motor imagery decoding.

Participants

20

Citations

14

HED

v8.4.0

Size

16.0 GB

Version

vv1.0.0

Updated

Jul 10, 2026