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nm000321
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study Q)

EEG

BigP3BCI Study Q is a P300-based brain-computer interface dataset comprising EEG recordings from 36 ALS subjects across 3 sessions each, using a 6x6 color intensification speller paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 electrode montage, annotated with target and non-target event labels for machine learning applications. 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

36

Channels

32 (10-10)

HED

v8.4.0

Size

1.09 GB

Version

vv1.0.1

Updated

Jul 10, 2026

by Boyla Mainsah et al.
nm000313
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study S2)

EEG

BigP3BCI Study S2 is a P300-based brain-computer interface dataset comprising EEG recordings from 24 healthy subjects performing a 9x8 house/tool visual speller paradigm. This derivative dataset, part of the larger bigP3BCI collection, contains standardized EEG data acquired at 256 Hz from 32 channels using g.USBamp hardware. The dataset is designed for machine learning applications and BCI research, with target and non-target event classifications annotated using HED 8.4.0 standards.

Participants

24

Channels

32 (10-10)

HED

v8.4.0

Size

1.11 GB

Version

vv1.0.1

Updated

Jul 10, 2026

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

EEG

Participants

25

Channels

71 (10-10)

Citations

80

HED

v8.4.0

Size

88.6 GB

Version

vv1.0.0

Updated

Jul 10, 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 64-channel EEG recordings plus 1 EOG and 1 stimulus channel 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

9

HED

v8.4.0

Size

2.06 GB

Version

vv1.0.1

Updated

Jul 10, 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. Participants performed a visual speller task using a 9x8 character grid with supervised/checkerboard stimulus paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz, annotated with target and non-target event labels, and is part of the larger BigP3BCI collection representing one of the most comprehensive public P300 BCI datasets.

Participants

18

Channels

32 (10-10)

HED

v8.4.0

Size

981 MB

Version

vv1.0.1

Updated

Jul 10, 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 using dynamic row-column stimulus presentation. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 electrode montage, annotated with target and non-target event labels using HED 8.4.0 schema. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is designed for machine learning model development and benchmarking in brain-computer interface research.

Participants

17

Channels

32 (10-10)

HED

v8.4.0

Size

731 MB

Version

vv1.0.1

Updated

Jul 10, 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 Study G 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

327 MB

Version

vv1.0.1

Updated

Jul 10, 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

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

vv1.0.1

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

2

Size

3.46 GB

Version

vv1.0.1

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

20

Size

5.79 GB

Version

vv1.0.1

Updated

Jul 10, 2026

by Yuan Liu et al.