nm000206
Neuroergonomic 2021 dataset
EEG
…Passive BCI neuroergonomics dataset with resting state and 3 difficulty levels of…
- Participants
- 15
- Citations
- 56
- HED
- v8.4.0
- Size
- 1.22 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
Discover datasets
81 results for "neuroergonomics" · page 1 of 9 · ranked by relevance
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- nm000343
Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications
EEG…Passive BCI neuroergonomics dataset with resting state and 3 difficulty levels of…
- Participants
- 15
- Channels
- 61 (10-10)
- Citations
- 59
- HED
- v8.4.0
- Size
- 1.22 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- nm000243
BNCI 2016-002 Emergency Braking during Simulated Driving dataset
EEG…driving_assistance, emergency_braking_detection, neuroergonomics - **Environment**: laboratory - **Online feedback**: True ## Tags…
- Participants
- 15
- Citations
- 81
- HED
- v8.4.0
- Size
- 4.01 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- on004350
Executive Functionning Study for Assessing the Effect of Neurofeedback
EEG- Participants
- 24
- Channels
- 64 (10-10)
- Citations
- 48
- Size
- 26.4 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- on007788
Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals
EEG MOTIONThis longitudinal brain-machine interface study presents multimodal neurophysiological and kinematic data from seven healthy adults performing motor imagery tasks to control a lower-limb exoskeleton. The dataset comprises 60-channel EEG with EOG, dual inertial measurement units, and exoskeleton control signals collected across nine sessions per participant. Data include both open-loop calibration phases and closed-loop BMI control trials, providing a comprehensive resource for investigating neural correlates of motor imagery and human-machine interaction in ambulatory tasks.
- Participants
- 7
- Channels
- 60
- Size
- 2.98 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000178
BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset
EEGThis dataset comprises EEG recordings from 15 healthy participants performing self-initiated reach-and-grasp motor tasks using three different recording systems: gel-based laboratory equipment, water-based mobile EEG, and dry-electrode mobile EEG. Data were acquired at 256 Hz from 58 EEG channels plus 6 EOG channels across three sessions with a total of 7,200 trials. Participants executed palmar and lateral grasp actions toward objects while EEG signals were recorded. The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.
- Participants
- 45
- Channels
- 58 (10-05)
- HED
- v8.4.0
- Size
- 2.08 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- nm000163
c-VEP and Burst-VEP dataset from Castillos et al. (2023)
EEG…Human Factors and Neuroergonomics - **Address**: 10 Av. Edouard Belin, Toulouse, 31400, France…
- Participants
- 12
- Channels
- 32 (10-10)
- Citations
- 20
- HED
- v8.4.0
- Size
- 158 MB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000104
emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography
EMGNEMAR Dataset nm000104: emg2qwerty - EMG-based typing detection
- Participants
- 108
- Size
- 372 GB
- Version
- vv2.0.0
- Updated
- Jul 10, 2026
- nm000344
CastillosBurstVEP100
EEG…Human Factors and Neuroergonomics Address: 10 Av. Edouard Belin, Toulouse, 31400, France…
- Participants
- 12
- Citations
- 20
- HED
- v8.4.0
- Size
- 148 MB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- on005342
EEG data offline and online during motor imagery for standing and sitting
EEGThis dataset comprises electroencephalographic recordings from 32 participants performing motor imagery tasks during sit-to-stand and stand-to-sit transitions in both offline and online brain-computer interface (BCI) paradigms. Participants completed guided motor imagery trials while seated or standing, with EEG signals recorded from 17 channels at 250 Hz. The dataset includes offline calibration phases used to train machine learning classifiers and corresponding online validation phases where real-time BCI decoding was performed, providing a comprehensive resource for investigating neural correlates of postural transitions and BCI performance.
- Participants
- 32
- Channels
- 17 (10-10)
- Size
- 2.03 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026