nm000145
Munich Motor Imagery dataset
EEG
- Size
- 5.42 GB
- Updated
- May 25, 2026
Discover datasets
95 results for "fMRI" · page 8 of 10 · ranked by relevance
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- nm000351
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study P)
EEGBigP3BCI Study P is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects across 2 sessions each, using a 9x8 character grid spelling paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 montage, annotated with target and non-target visual stimulus events. This derivative dataset is part of the largest public P300 BCI dataset collection, and is optimized for machine learning applications in BCI research.
- Size
- 1.46 GB
- Updated
- Jun 3, 2026
- nm000144
BNCI 2015-004 Mental tasks dataset
EEG- Size
- 1.08 GB
- Updated
- May 16, 2026
- nm000253
Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli
iEEGBrain Treebank is a large-scale intracranial EEG dataset comprising 43 hours of iEEG recordings from 10 epilepsy patients watching naturalistic Hollywood movies, with 1,688 electrodes sampled at 2048 Hz. The dataset includes time-aligned linguistic annotations with word-level transcripts and Universal Dependencies syntax trees, providing a unique resource for studying neural language processing during naturalistic stimulation.
- Size
- 508 GB
- Updated
- Jun 8, 2026
- nm000199
Learning from label proportions for a visual matrix speller (ERP)
EEG- Size
- 5.14 GB
- Updated
- Jun 2, 2026
- nm000338
Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
EEGA comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms—motor imagery, event-related potentials, and steady-state visually evoked potentials—across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that motor imagery exhibits the highest illiteracy rate (53.7%) compared to other paradigms, while all participants demonstrated proficiency with at least one BCI system.
- Size
- 60.8 GB
- Updated
- Jun 3, 2026
- ds004995
The Time-Course of Food Representation in the Human Brain
EEG- Participants
- 20
- 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
- ds005169
Dataset of intracranial EEG during cortical stimulation evoking visual effects
iEEG MRI- Participants
- 20
- ds004514
Simultaneous recordings of EEG and fNIRS of semantic categories of imagined concepts of animals and tools
EEG NIRS- Participants
- 12