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nm000268
Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification

EEG

This open-access hybrid brain-computer interface dataset combines simultaneous EEG and near-infrared spectroscopy (NIRS) recordings from 29 healthy subjects performing mental arithmetic and motor imagery tasks. Experiment B focuses on mental arithmetic (serial subtraction) versus rest conditions across six sessions with 20 trials per session. The dataset includes preprocessed EEG data (30 channels, 200 Hz sampling rate) with comprehensive artifact correction and has been validated for single-trial classification using common spatial patterns and linear discriminant analysis.

Participants

29

Channels

30 (10-05)

Citations

94

HED

v8.4.0

Size

1.87 GB

Version

vv1.0.1

Updated

Jul 10, 2026

by Jaeyoung Shin et al.
nm000267
Shin et al. 2017 (Experiment A) — Open Access Dataset for EEG+NIRS Single-Trial Classification

EEG

An open access hybrid brain-computer interface dataset combining electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings from 29 healthy subjects performing motor imagery and mental arithmetic tasks. The dataset includes preprocessed EEG data (30 channels, 200 Hz sampling rate) and NIRS data with validated classification performance demonstrating enhanced discrimination of mental states through multimodal analysis compared to single modality approaches.

Participants

29

Channels

30 (10-05)

Citations

94

HED

v8.4.0

Size

1.87 GB

Version

vv1.0.1

Updated

Jul 10, 2026

by Jaeyoung Shin et al.
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, designed to evaluate Bayesian optimization strategies for automated selection of individually optimal stimulation parameters in brain-computer interface applications.

Participants

13

Channels

31 (10-10)

Citations

2

HED

v8.4.0

Size

3.66 GB

Version

vv1.0.1

Updated

Jul 10, 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 and eye-tracking recordings from 31 healthy participants performing motor imagery tasks. The dataset comprises 2,520 trials across 63 sessions, with participants imagining left and right hand movements in response to visual cues. Recorded at 1000 Hz using a 64-channel Neuroscan system, this dataset supports brain-computer interface research and motor imagery analysis.

Participants

31

Channels

62 (10-10)

Citations

8

HED

v8.4.0

Size

9.22 GB

Version

vv1.0.1

Updated

Jul 10, 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 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

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. 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

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 comprises EEG recordings from 10 healthy participants performing a P300 speller task using a 6×6 character matrix. The study compares two stimulus conditions—famous faces and inverting—to evaluate their effects on online P300 classification performance using language models. Data were collected across 2 sessions per subject with 3 runs per session, sampled at 256 Hz from 32 EEG channels.

Participants

10

Channels

32 (10-10)

Citations

28

HED

v8.4.0

Size

288 MB

Version

vv1.0.1

Updated

Jul 10, 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 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

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 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

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 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

by Boyla Mainsah et al.