nm000205
RSVP collaborative BCI dataset from Zheng et al 2020
EEG
…A Cross-Session Dataset for Collaborative Brain-Computer Interfaces Based on Rapid…
- Participants
- 14
- Citations
- 26
- HED
- v8.4.0
- Size
- 5.29 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "neuromotor interfaces" · page 6 of 10 · ranked by relevance
-
- nm000173
Motor Imagery ataset from Ofner et al 2017
EEG- Participants
- 15
- Channels
- 61 (10-05)
- Citations
- 95
- HED
- v8.4.0
- Size
- 8.54 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- on006593
cBCI Matrix Multimodal Dataset
EEG…Brain-Computer Interfaces, 8(4), 137-53, 2021.
- Participants
- 21
- Channels
- 19 (10-20)
- Size
- 615 MB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000168
BNCI 2015-013 Error-Related Potentials dataset
EEG…Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE…
- Participants
- 6
- Channels
- 64 (10-10)
- Citations
- 84
- HED
- v8.4.0
- Size
- 2.02 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 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.
- Participants
- 20
- Channels
- 32 (10-10)
- HED
- v8.4.0
- Size
- 1.95 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- on002722
A dataset recorded during development of an affective brain-computer music interface: calibration session
EEG…4th Workshop on Affective Brain-Computer Interfaces at the 6th International Conference…
- Participants
- 19
- Channels
- 32 (10-10)
- Size
- 6.10 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- on006035
somatomotor
ANAT MEGA multi-modal neuroimaging dataset combining structural MRI, magnetoencephalography (MEG), and electroencephalography (EEG) recordings from multiple subjects during median nerve electrical stimulation and motor response tasks. Participants performed rapid left index finger lifts in response to right median nerve stimuli, providing a comprehensive resource for investigating somatosensory processing and motor control mechanisms.
- Participants
- 5
- Size
- 3.07 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000198
BNCI 2015-008 Center Speller P300 dataset
EEG…Gaze-independent brain-computer interfaces based on covert attention and feature attention…
- Participants
- 13
- Citations
- 81
- HED
- v8.4.0
- Size
- 3.08 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000220
BEETL Competition 2021 Motor Imagery Dataset A — transfer learning benchmark
EEGBeetl2021-A is a preprocessed motor imagery EEG dataset from the BEETL Competition Task 2 (NeurIPS 2021), comprising data from 3 healthy subjects collected during an online racing game (Cybathlon2020IC). The dataset contains 63-channel EEG recordings at 500 Hz with four-class motor imagery tasks (rest, left hand, right hand, feet) and serves as a benchmark for evaluating transfer learning and domain adaptation methods across heterogeneous EEG datasets and subjects. This dataset is part of a larger competition focused on advancing transfer learning for subject independence and cross-dataset generalization in brain-computer interfacing.
- Participants
- 3
- Channels
- 63 (10-10)
- HED
- v8.4.0
- Size
- 216 MB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- nm000194
BNCI 2015-010 RSVP P300 dataset
EEG…Brain Computer Interfaces, RSVP, ERPs, Speller, P300, N2, gaze-independent ## Abstract A…
- Participants
- 12
- Citations
- 95
- HED
- v8.4.0
- Size
- 2.06 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026