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nm000266
Sosulski et al. 2019 — Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints

EEG

An auditory event-related potential dataset from 13 healthy subjects performing an oddball paradigm with two sinusoidal tones (target 1000 Hz, non-target 500 Hz) presented at variable stimulus onset asynchronies (60-600 ms). The dataset comprises 31-channel EEG recordings at 1000 Hz acquired with BrainProducts BrainAmp DC during an online closed-loop brain-computer interface experiment designed to optimize stimulation parameters using Bayesian optimization and random search strategies.

Participants

13

Channels

31 (10-10)

Citations

2

HED

v8.4.0

Size

3.68 GB

Version

v1.0.4

Updated

Aug 18, 2026

by Jan Sosulski et al.
nm000265
Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms

EEG

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery tasks. The dataset comprises 2,520 trials across 63 sessions, with participants performing left and right hand motor imagery in response to visual cues. Recorded at 1000 Hz using a 64-channel EEG montage with standardized electrode placement, this dataset supports brain-computer interface research and analysis of ocular activity patterns during motor imagery paradigms.

Participants

31

Channels

62 (10-10)

Citations

11

HED

v8.4.0

Size

33.6 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Eva Guttmann-Flury et al.
nm000264
Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a)

EEG

This dataset comprises electroencephalographic recordings from 24 healthy subjects performing a visual P300 brain-computer interface task based on the Brain Invaders video game. 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

3.00 GB

Version

v1.0.4

Updated

Aug 18, 2026

by E. Vaineau et al.
nm000260
Van Veen, Barachant & Andreev 2012 — Building Brain Invaders: EEG data of an experimental validation (BI2012)

EEG

This dataset comprises electroencephalographic recordings from 25 healthy subjects performing a visual P300 brain-computer interface task based on the Brain Invaders paradigm, a game inspired by Space Invaders. The experiment involved 16-channel EEG recordings at 128 Hz sampling rate during both training and online sessions with visual stimulus presentations. The dataset includes 128 target trials and 640 non-target trials per subject, with event-related potentials peaking 240-600 ms after stimulus onset.

Participants

23

Channels

17 (10-20)

Citations

3

HED

v8.4.0

Size

312 MB

Version

v1.0.4

Updated

Aug 18, 2026

by G.F.P. Van Veen et al.
nm000259
Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models

EEG

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

by William Speier et al.
nm000250
Dreyer et al. 2023 — A large EEG database with users' profile information for motor imagery brain-computer interface research

EEG

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

by Pauline Dreyer et al.
nm000249
BNCI 2022-001 EEG Correlates of Difficulty Level dataset

EEG

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

by Ping-Keng Jao et al.
nm000248
BigP3BCI Study L — 6x6 multi-paradigm (11 ALS subjects)

EEG

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

by Boyla Mainsah et al.
nm000247
BigP3BCI Study S1 — 9x8 face/house paradigm (10 healthy subjects)

EEG

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

by Boyla Mainsah et al.
nm000246
Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025

EEG

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

by Banghua Yang et al.