on003194
Neuroepo multisession
EEG
Imported from OpenNeuro ds003194
- Participants
- 15
- Channels
- 19 (10-20)
- Size
- 189 MB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
10 results for "neuroprosthetics" · ranked by relevance
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- on004279
Large Spanish EEG
EEG[
- Size
- 25.2 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000206
Neuroergonomic 2021 dataset
EEGA multi-session EEG dataset acquired from 15 healthy participants performing resting state and graded cognitive tasks (MATB-II at three difficulty levels). The dataset comprises 62-channel EEG recordings at 500 Hz sampling rate designed for passive brain-computer interface applications and mental workload estimation in neuroergonomic contexts. Raw EEG data are provided with standardized event annotations using HED 8.4.0 schema and MOABB-compatible feature extraction pipelines (bandpower analysis and Riemannian covariance methods) for benchmarking purposes.
- Participants
- 15
- Citations
- 61
- HED
- v8.4.0
- Size
- 1.22 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000168
BNCI 2015-013 Error-Related Potentials dataset
EEG…Defitech Chair in Brain-Machine Interface, CNBI, Center for Neuroprosthetics - **Country**: CH…
- Participants
- 6
- Channels
- 64 (10-10)
- Citations
- 83
- HED
- v8.4.0
- Size
- 5.21 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026
- on006803
NeuroTechs Dataset for Stem Skills
EEGThis dataset contains EEG recordings collected as part of the NeuroTechs project examining STEM skills. Data were acquired using the Unicorn EEG device and converted to BIDS format, with signal values expressed in microvolts. The dataset is intended to support research on neural correlates of STEM-related cognitive processes.
- Participants
- 63
- Channels
- 8 (10-10)
- Size
- 1.39 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000189
BNCI 2015-003 P300 dataset
EEG…http://www.frontiersin.org/neuroprosthetics/10.3389/fnins.2011.00112/abstract ## Abstract…
- Participants
- 10
- Channels
- 8 (10-10)
- Citations
- 81
- HED
- v8.4.0
- Size
- 74.4 MB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- nm000178
BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset
EEGThis dataset comprises EEG recordings from 15 healthy participants performing reach-and-grasp motor imagery tasks using three different electrode 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 7200 total trials. Participants executed palmar grasp (toward glass) and lateral grasp (toward spoon) actions. 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)
- Citations
- 51
- HED
- v8.4.0
- Size
- 8.67 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- on005692
SpinalExpect_NonInvasive
EEGThis dataset contains non-invasive electrophysiological recordings from the human spinal cord in 30 healthy young adults during a temporal expectation paradigm. Subjects received electric median nerve stimulation preceded by an auditory cue with either fixed or variable timing, allowing investigation of whether temporal expectation modulates initial sensory processing at the spinal level. Recordings used a specialized multi-ring electrode montage around the neck to capture spinal cord signals.
- Participants
- 30
- Channels
- 24 (other)
- Size
- 92.8 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on003195
Placebo Neuroepo multisession
EEGImported from OpenNeuro ds003195
- Participants
- 10
- Channels
- 19 (10-20)
- Citations
- 1
- Size
- 121 MB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000264
Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a)
EEGThis dataset comprises electroencephalographic recordings from 24 healthy subjects performing a visual P300 brain-computer interface task based on the Brain Invaders video game. The study compares adaptive versus non-adaptive calibration approaches using Riemannian geometry for classifier training. EEG data were acquired using 16 electrodes at 512 Hz sampling rate during sessions conducted at GIPSA-lab, Grenoble, France in 2013.
- Participants
- 24
- Channels
- 16 (10-10)
- Citations
- 6
- HED
- v8.4.0
- Size
- 3.00 GB
- Version
- v1.0.4
- Updated
- Aug 18, 2026