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nm000272
Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)

EEG

BrainForm is a P300 event-related potential (ERP) EEG dataset collected from 22 participants using a gamified brain-computer interface (BCI) designed for scalable data collection with consumer hardware. Participants completed repeated runs of a P300 spelling/selection task within a serious game paradigm, enabling investigation of BCI skill acquisition across sessions and the effects of different visual stimulation textures on perceptual and performance outcomes.

Participants

22

Citations

1

Size

132 MB

Version

v1.0.1

Updated

Jul 10, 2026

by Michele Romani et al.
nm000271
Chang et al. 2025 — A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises (Lanzhou Jiaotong University)

EEG

A multi-paradigm EEG dataset comprising scalp electroencephalography recordings from 28 participants performing and imagining upper-limb rehabilitation exercises. This dataset is designed to support research on motor imagery and rehabilitation brain-computer interfaces (BCI) for upper-limb motor function recovery and assessment.

Participants

28

Citations

3

Size

3.46 GB

Version

v1.0.1

Updated

Jul 10, 2026

by Wenwen Chang et al.
nm000270
Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)

EEG

This dataset comprises scalp electroencephalography recordings from 27 stroke patients performing lower-limb motor imagery tasks as part of a longitudinal rehabilitation training protocol. The multi-paradigm design supports research on motor-imagery brain–computer interfaces for gait and lower-limb rehabilitation following stroke, with repeated sessions enabling investigation of learning and neuroplasticity during motor recovery.

Participants

27

Citations

22

Size

5.79 GB

Version

v1.0.1

Updated

Jul 10, 2026

by Yuan Liu et al.
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

v1.0.1

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

v1.0.1

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

v1.0.1

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

11

HED

v8.4.0

Size

9.22 GB

Version

v1.0.1

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

v1.0.1

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

v1.0.1

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

v1.0.1

Updated

Jul 10, 2026

by William Speier et al.