ds007521
The effect of hunger and state preferences on the neural processing of food images
EEG
…Human electroencephalography recordings from 23 participants, who did a letter task and…
- Participants
- 23
Discover datasets
100 results for "high-density electroencephalography" · page 10 of 10 · ranked by relevance
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- on002578
Visual Oddball Task (256 channels)
ANAT EEGImported from OpenNeuro ds002578
- Size
- 1.33 GB
- Updated
- May 26, 2026
- on004551
iEEG on children during slow wave sleep
iEEG[ - EEG
EEG…EEG-BIDS, an extension to the brain imaging data structure for electroencephalography…
- Participants
- 43
- ds007162
Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG)
EEG- Participants
- 34
- ds004582
FakeFaceEmo_data
EEG# Overview This dataset was collected in 2023 and comprises electroencephalography, physiological and…
- Participants
- 73
- 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
- nm000141
Motor execution dataset from Wairagkar et al 2018
EEG…50.0 Hz - **Online filters**: {'highpass': 0.5, 'lowpass': 60, 'notch_hz…
- Citations
- 9
- Size
- 571 MB
- Updated
- May 16, 2026
- nm000321
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study Q)
EEGBigP3BCI Study Q is a P300-based brain-computer interface dataset comprising EEG recordings from 36 ALS subjects across 3 sessions each, using a 6x6 color intensification speller paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 electrode montage, annotated with target and non-target event labels for machine learning applications. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with recordings from approximately 267 subjects across 20 studies.
- Size
- 1.09 GB
- Updated
- Jun 3, 2026
- ds005274
UV_EEG
EEG- Participants
- 22