nm000273
OpenBMI SSVEP EEG dataset (Lee et al. 2019)
EEG Zarr
…org/10.82901/nemar.nm000273) # OpenBMI SSVEP EEG dataset (Lee et al…
- Participants
- 54
- Channels
- 62 (10-05)
- Citations
- 101
- Size
- 72.4 GB
- Version
- v1.0.8
- Updated
- Oct 8, 2026
Discover datasets
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- nm000323
Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
EEG Zarr…EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into…
- Participants
- 54
- Channels
- 62 (10-05)
- Citations
- 100
- HED
- v8.4.0
- Size
- 132 GB
- Version
- v1.0.5
- Updated
- Oct 8, 2026
- nm000338
Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
EEG Zarr…EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into…
- Participants
- 54
- Channels
- 62 (10-05)
- Citations
- 99
- HED
- v8.4.0
- Size
- 121 GB
- Version
- v1.0.5
- Updated
- Aug 18, 2026
- nm000301
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study D)
EEG ZarrStudy D of the bigP3BCI collection, comprising 32-channel EEG recordings from 17 healthy participants using a P300-based speller with a 6x6 character grid and dynamic row-column stimulus presentation. Data were recorded in a single session per subject with online feedback, and events are labeled as target or non-target flashes. The dataset is a BIDS-formatted derivative of the PhysioNet bigP3BCI release, intended for machine learning and BCI benchmarking.
- Participants
- 17
- Channels
- 32 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 2.41 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- nm000340
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study J)
EEG ZarrStudy J of the bigP3BCI collection, which provides EEG recordings from 20 healthy participants using a P300-based speller. The task used a 9x8 character grid with a performance-based row-column paradigm, and 16 EEG channels were recorded at 256 Hz. Target and non-target flash events are annotated with HED tags, and the dataset supports machine-learning benchmarking of P300 brain-computer interfaces.
- Participants
- 20
- Channels
- 16 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 1.49 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- nm000303
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study O)
EEG ZarrThis dataset is Study O of bigP3BCI, a large open collection of P300-based brain-computer interface EEG recordings. It contains 32-channel EEG from 18 participants with ALS, each recorded over two sessions while using a 9x8 character speller with a supervised checkerboard stimulus paradigm. Target and non-target flashes are annotated with HED tags, and the data are formatted for machine learning and benchmarking.
- Participants
- 18
- Channels
- 32 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 3.23 GB
- Version
- v1.0.4
- Updated
- Oct 8, 2026
- nm000313
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study S2)
EEG ZarrThis dataset contains EEG recordings from 24 healthy participants in Study S2 of bigP3BCI, a large open P300-based brain-computer interface collection. Participants used a 9x8 character grid speller with a house/tool stimulus paradigm, with target and non-target events annotated using HED. Data were recorded at 256 Hz from 32 channels and are provided in a machine-learning-ready BIDS format via MOABB.
- Participants
- 24
- Channels
- 32 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 3.74 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- nm000336
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study R)
EEG ZarrThis dataset is Study R of bigP3BCI, a large open P300-based brain-computer interface collection. It contains 32-channel EEG recorded from 20 participants over two sessions each, using a 9x8 multi-face speller grid with target and non-target flashes. The data are standardized in BIDS and prepared for machine learning and benchmarking of P300 speller decoding.
- Participants
- 20
- Channels
- 32 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 6.59 GB
- Version
- v1.0.4
- Updated
- Oct 8, 2026
- on007720
BCI-FIT: A customization protocol for communication brain-computer interface systems.
EEG ZarrEEG recordings from five participants with speech and/or physical impairments due to amyotrophic lateral sclerosis (ALS) who used a non-implantable communication brain-computer interface (cBCI) over multiple visits. Participants completed calibration and copy-spelling tasks with Matrix and RSVP layouts, in both customized and non-customized system configurations. Customization was iteratively adjusted based on typing performance, preferences, clinical observations, brain signal characteristics, and offline simulations. The data are organized in BIDS format.
- Participants
- 5
- Channels
- 7 (10-10)
- Size
- 2.17 GB
- Version
- v1.0.0
- Updated
- Jun 19, 2026
- nm000277
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study G)
EEG ZarrThis dataset contains EEG recordings from 20 healthy participants in Study G of the bigP3BCI collection, a large open P300-based brain-computer interface resource. Participants used a 9x8 character grid speller with checkerboard/dynamic stimulus presentation. Data were recorded with 16 EEG channels at 256 Hz, and target and non-target events are annotated with HED tags. The recordings support machine learning development for P300 speller BCIs.
- Participants
- 20
- Channels
- 16 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 1.62 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026