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nm000326
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study C)

EEG Zarr

Study C of the bigP3BCI collection, which contains EEG recordings from 19 healthy participants using a 6x6 checkerboard P300 speller paradigm with error-related negativity (ERN). Data were recorded with 32 channels at 256 Hz and annotated with target and non-target events using HED. bigP3BCI is a large, diverse, machine-learning-ready public P300 BCI dataset spanning many studies.

Participants

19

Channels

32 (10-10)

Citations

2

HED

v8.4.0

Size

4.11 GB

Version

v1.0.4

Updated

Oct 8, 2026

by Boyla Mainsah et al.
nm000323
Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

EEG Zarr

EEG recordings from 54 healthy participants performing a 36-symbol ERP (P300) row-column speller task with face stimuli, collected over two sessions with offline training and online copy-spelling test phases. Data were recorded at 1000 Hz from 62 EEG channels plus 4 additional EMG channels. The dataset is part of a three-paradigm BCI collection (ERP, SSVEP, motor imagery) created to investigate BCI illiteracy and distributed with the OpenBMI toolbox.

Participants

54

Channels

62 (10-05)

Citations

100

HED

v8.4.0

Size

132 GB

Version

v1.0.5

Updated

Oct 8, 2026

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

EEG Zarr

This 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

by Boyla Mainsah et al.
nm000338
Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

EEG Zarr

A motor imagery EEG dataset from 54 healthy participants performing binary left/right hand grasping imagery across two sessions on different days. Recordings were made with 62 EEG channels plus 4 EMG channels at 1000 Hz. The data belong to a larger multi-paradigm BCI collection (MI, ERP, SSVEP) used to investigate BCI illiteracy and variation in decoding performance across subjects and sessions.

Participants

54

Channels

62 (10-05)

Citations

99

HED

v8.4.0

Size

121 GB

Version

v1.0.5

Updated

Aug 18, 2026

by Min-Ho Lee et al.
nm000249
BNCI 2022-001 EEG Correlates of Difficulty Level dataset

EEG Zarr

EEG recordings from 13 healthy participants who piloted a simulated drone through circular waypoints at varying difficulty levels using a joystick. The dataset includes an offline session with a v-shaped difficulty design and two online sessions in which subjective difficulty was decoded from EEG to support closed-loop adaptation of task difficulty. Data were recorded with a 64-channel Biosemi ActiveTwo system and are provided preprocessed in BIDS format via the MOABB collection (BNCI 2022-001).

Participants

13

Channels

64 (10-10)

Citations

24

HED

v8.4.0

Size

9.42 GB

Version

v1.0.2

Updated

Aug 18, 2026

by Ping-Keng Jao et al.
nm000170
BNCI 2025-002 Continuous 2D Trajectory Decoding dataset

EEG Zarr

EEG recordings from 10 able-bodied participants, plus a feasibility recording from one participant with spinal cord injury, performing attempted lower-arm movements to control a continuous 2D cursor trajectory. Each of the able-bodied participants completed three sessions comprising offline calibration and online feedback with 50% and 100% EEG control. The dataset supports regression-based trajectory decoding, learning effects across sessions, and source-localization analyses for brain-computer interface research.

Participants

10

Channels

60 (10-05)

Citations

20

HED

v8.4.0

Size

7.38 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Hannah S Pulferer 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 Zarr

A multimodal SSVEP dataset of 64-channel EEG, an EOG channel, and stimulus markers recorded from 31 healthy participants over up to 3 sessions each (63 sessions in total). Participants viewed LED stimuli flickering at four frequencies in a 4-class SSVEP paradigm (48 trials per session). It is the SSVEP component of a larger resource combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms, and is formatted for use with MOABB.

Participants

11

Channels

62 (10-10)

Citations

14

HED

v8.4.0

Size

28.6 GB

Version

v1.0.4

Updated

Oct 8, 2026

by Eva Guttmann-Flury et al.
nm000136
Guttmann-Flury et al. 2025 (P300) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms

EEG Zarr

This dataset contains 64-channel EEG recorded at 1000 Hz from 31 healthy participants performing a row/column P300 speller task with 4L and 5L grid sizes. Each participant completed three sessions. It is the P300 component of a multimodal eye-BCI collection that also includes eye-tracking and high-speed video for analyzing ocular activity across BCI paradigms. Events are annotated with HED tags as Target and NonTarget flashes.

Participants

31

Channels

62 (10-10)

Citations

13

HED

v8.4.0

Size

40.7 GB

Version

v1.0.4

Updated

Oct 8, 2026

by Eva Guttmann-Flury et al.
nm000336
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study R)

EEG Zarr

This 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

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

EEG Zarr

This 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

by Boyla Mainsah et al.