metadata
pretty_name: GuttmannFlury2025-P300
license: cc0-1.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
- visual
- attention
size_categories:
- n<1K
task_categories:
- other
GuttmannFlury2025-P300
Dataset ID: nm000136
GuttmannFlury2025
At a glance: EEG · Visual attention · healthy · 31 subjects · 63 recordings · CC0
Load this dataset
This repo is a pointer. The raw EEG data lives at its canonical source (OpenNeuro / NEMAR); EEGDash streams it on demand and returns a PyTorch / braindecode dataset.
# pip install eegdash
from eegdash import EEGDashDataset
ds = EEGDashDataset(dataset="nm000136", cache_dir="./cache")
print(len(ds), "recordings")
If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout, you can also pull it directly:
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/nm000136")
Dataset metadata
| Subjects | 31 |
| Recordings | 63 |
| Tasks (count) | 1 |
| Channels | 65 (×63) |
| Sampling rate (Hz) | 1000 (×63) |
| Total duration (h) | 11.2 |
| Size on disk | 7.3 GB |
| Recording type | EEG |
| Experimental modality | Visual |
| Paradigm type | Attention |
| Population | Healthy |
| Source | nemar |
| License | CC0 |
Links
- DOI: 10.1038/s41597-025-04861-9
- NEMAR: nm000136
- Browse 700+ datasets: EEGDash catalog
- Docs: https://eegdash.org
- Code: https://github.com/eegdash/EEGDash
Auto-generated from dataset_summary.csv and the EEGDash API. Do not edit this file by hand — update the upstream source and re-run scripts/push_metadata_stubs.py.