on004121
BCIT Mind Wandering
EEG
…Subjects were then outfitted and prepped for eye tracking and EEG acquisition…
- Participants
- 21
- Channels
- 64 (10-10)
- Citations
- 32
- HED
- v8.0.0
- Size
- 23.9 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
Discover datasets
100 results for "neural tracking" · page 5 of 10 · ranked by relevance
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- on004952
ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding
EEG…A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding…
- Participants
- 10
- Channels
- 128 (egi-geodesic)
- Citations
- 24
- Size
- 697 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on006802
Collaborative rule learning promotes interbrain information alignment
EEG[.
- Participants
- 22
- Size
- 69.7 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on004122
BCIT Speed Control
EEG…Eye tracking data is not included with this dataset. **Initial setup:** Upon…
- Participants
- 32
- Channels
- 64 (10-10)
- Citations
- 41
- HED
- v8.0.0
- Size
- 36.2 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000184
BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU)
EEGThis dataset comprises EEG recordings from 15 healthy participants performing a visual P300 oddball task during treadmill walking at 1.6 m/s. The study includes simultaneous scalp-EEG (46 channels), ear-EEG (14 channels), electrooculography (4 channels), and inertial measurement unit recordings (6 channels) sampled at 100 Hz. The dataset was collected as part of BCI Competition 2020 Track 5 to evaluate brain-computer interface performance under ambulatory conditions. This is a BIDS-formatted derivative dataset converted from the original data using the MOABB (Mother of All BCI Benchmarks) pipeline.
- Participants
- 15
- Channels
- 46 (10-10)
- Citations
- 71
- HED
- v8.4.0
- Size
- 224 MB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- on002312
OcularLDT
MEG[
EEGThis dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.
- Participants
- 13
- Channels
- 31 (10-10)
- Citations
- 46
- HED
- v8.4.0
- Size
- 5.15 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026
- on004123
BCIT Traffic Complexity
EEG…Subjects were then outfitted and prepped for eye tracking and EEG acquisition…
- Participants
- 29
- Channels
- 64 (10-10)
- Citations
- 39
- HED
- v8.0.0
- Size
- 17.5 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on006817
Visual Attribute-Specific Contextual Trajectory Paradigm 2.0
EEGThis dataset comprises EEG recordings collected using a Visual Attribute-Specific Contextual Trajectory Paradigm, designed to investigate visual attention and perceptual processing of moving stimuli. It provides raw electrophysiological data intended to support research into contextual and attribute-specific visual trajectory processing in humans.
- Participants
- 35
- Citations
- 1
- Size
- 11.4 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026