ds004624
Intracranial recordings using BCI2000 and the CorTec BrainInterchange
iEEG MRI
- Participants
- 2
Discover datasets
97 results for "Neuroergonomics" · page 7 of 10 · ranked by relevance
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- nm000162
BNCI 2025-001 Motor Kinematics Reaching dataset
EEG- Size
- 15.0 GB
- Updated
- May 18, 2026
- nm000336
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study R)
EEGBigP3BCI Study R is a P300-based brain-computer interface dataset comprising EEG recordings from 20 ALS subjects performing a 9x8 character grid speller task across two sessions. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects across 20 studies. The data were acquired at 256 Hz using 32-channel EEG with a g.USBamp amplifier and are organized in BIDS format with HED event annotations for standardized analysis and machine learning applications.
- Size
- 1.95 GB
- Updated
- Jun 3, 2026
- nm000132
ERP CORE
EEG- Size
- 17.5 GB
- Updated
- May 18, 2026
- nm000139
BNCI 2014-001 Motor Imagery dataset
EEG- Size
- 672 MB
- Updated
- May 16, 2026
- nm000159
MUniverse Avrillon et al 2024
EMGBIDS-EMG dataset - HDsEMG recordings of tibialis anterior and vastus lateralis during isometric contractions at 10-80% MVC, 16 subjects, 256-channel grids (MUniverse benchmark)
- Size
- 5.51 GB
- Updated
- Jun 1, 2026
- on003801
Neural Tracking to go
EEGImported from OpenNeuro ds003801
- Size
- 1.15 GB
- Updated
- May 23, 2026
- ds004166
Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial
EEG- Participants
- 71
- nm000351
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study P)
EEGBigP3BCI Study P is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects across 2 sessions each, using a 9x8 character grid spelling paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 montage, annotated with target and non-target visual stimulus events. This derivative dataset is part of the largest public P300 BCI dataset collection, and is optimized for machine learning applications in BCI research.
- Size
- 1.46 GB
- Updated
- Jun 3, 2026
- nm000338
Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
EEGA comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms—motor imagery, event-related potentials, and steady-state visually evoked potentials—across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that motor imagery exhibits the highest illiteracy rate (53.7%) compared to other paradigms, while all participants demonstrated proficiency with at least one BCI system.
- Size
- 60.8 GB
- Updated
- Jun 3, 2026