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Showing 10 of 673 datasets · page 54 of 68

nm000311
Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)

EEG

A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG, 4 EOG, 7 EMG channels) sampled at 1000 Hz with synchronized behavioral annotations, designed for brain-computer interface research and motor control applications. This is a BIDS-formatted derivative dataset converted from the original Jeong et al. 2020 publication using MOABB (Mother of All BCI Benchmarks).

Participants

25

Channels

71 (10-10)

Citations

82

HED

v8.4.0

Size

326 GB

Version

v1.0.2

Updated

Aug 18, 2026

by Ji-Hoon Jeong et al.
nm000310
Guttmann-Flury et al. 2025 (SSVEP) — 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 a 4-class steady-state visually evoked potential (SSVEP) brain-computer interface task. The dataset comprises 3,024 trials across 63 sessions with 66-channel recordings (64 EEG + 1 EOG + 1 stim) at 1000 Hz sampling rate, designed to investigate ocular activity patterns and their relationship to BCI performance across multiple paradigms.

Participants

11

Channels

62 (10-10)

Citations

12

HED

v8.4.0

Size

28.6 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Eva Guttmann-Flury et al.
nm000303
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study O)

EEG

BigP3BCI Study O is a P300-based brain-computer interface dataset comprising EEG recordings from 18 ALS subjects across 2 sessions each, using a 9x8 character grid with supervised and checkerboard stimulus paradigms. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 electrode montage, designed for machine learning applications in BCI speller systems. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with recordings from approximately 267 subjects across 20 studies.

Participants

18

Channels

32 (10-10)

HED

v8.4.0

Size

3.23 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
nm000301
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study D)

EEG

BigP3BCI Study D is a P300-based brain-computer interface dataset comprising EEG recordings from 17 healthy subjects performing a 6x6 character grid speller task. The dataset contains single-session recordings acquired at 256 Hz using 32-channel EEG with standard 10-20 montage, annotated with target and non-target event labels. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is formatted according to BIDS standards with HED event annotations for machine learning applications.

Participants

17

Channels

32 (10-10)

HED

v8.4.0

Size

2.41 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
nm000277
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study G)

EEG

BigP3BCI Study G is a P300-based brain-computer interface dataset comprising EEG recordings from 20 healthy subjects performing a 9x8 checkerboard visual speller task. This derivative dataset is one of 20 studies in the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects total. The data were acquired at 256 Hz using 16-channel EEG with a g.USBamp amplifier and include target and non-target event classifications suitable for machine learning applications.

Participants

20

Channels

16 (10-10)

HED

v8.4.0

Size

1.62 GB

Version

v1.0.3

Updated

Aug 18, 2026

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

EEG

This dataset comprises scalp EEG recordings from 22 participants who used BrainForm, a gamified brain-computer interface (BCI) designed for P300 event-related potential (ERP) training and scalable data collection with consumer hardware. Participants performed repeated runs of a P300 spelling/selection task, allowing analysis of BCI skill acquisition across sessions and the effects of different visual stimulation textures on performance. The dataset supports research on BCI training, human factors, and machine learning applications on ERP data.

Participants

22

Channels

8 (10-10)

Citations

1

HED

v8.4.0

Size

237 MB

Version

v1.0.6

Updated

Aug 20, 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

This dataset contains scalp EEG recordings from 28 participants performing or imagining upper-limb rehabilitation exercises under a multi-paradigm protocol. It was designed to support research on motor-imagery-based brain–computer interfaces (BCI) for upper-limb rehabilitation. The data have been converted to BIDS format from the original dataset published by Chang et al. (2025).

Participants

28

Channels

59 (10-10)

Citations

3

HED

v8.4.0

Size

47.1 GB

Version

v1.0.5

Updated

Aug 20, 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 EEG recordings from 27 stroke patients performing a lower-limb motor imagery task as part of a multi-paradigm, longitudinal rehabilitation training protocol. Data were collected at Tianjin University to support research on motor-imagery brain-computer interfaces for gait and lower-limb rehabilitation after stroke. The dataset is organized in BIDS format with a single EEG task (task-imagery) across repeated sessions.

Participants

27

Channels

64 (10-10)

Citations

23

HED

v8.4.0

Size

56.7 GB

Version

v1.0.5

Updated

Aug 20, 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 preprocessed data at 200 Hz sampling rate. The dataset includes comprehensive preprocessing (bandpass filtering, ICA-based artifact rejection, common average referencing) and serves as a benchmark for validating hybrid BCI approaches that integrate multimodal neuroimaging signals.

Participants

29

Channels

30 (10-05)

Citations

94

HED

v8.4.0

Size

6.62 GB

Version

v1.0.3

Updated

Aug 18, 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 EEG channels plus 2 EOG channels, 200 Hz sampling rate) and NIRS measurements (36 channels) across six sessions per subject, 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

6.50 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Jaeyoung Shin et al.