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MEG-BIDS OMEGA RestingState_sample
Dataset ID: ds000247
Niso2018
Canonical aliases: OMEGA
At a glance: MEG · Resting State resting-state · healthy · 6 subjects · 10 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="ds000247", cache_dir="./cache")
print(len(ds), "recordings")
You can also load it by canonical alias — these are registered classes in eegdash.dataset:
from eegdash.dataset import OMEGA
ds = OMEGA(cache_dir="./cache")
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/ds000247")
Dataset metadata
| Subjects | 6 |
| Recordings | 10 |
| Tasks (count) | 2 |
| Channels | 297 (×5), 330 (×3), 300 (×2) |
| Sampling rate (Hz) | 2400 (×10) |
| Total duration (h) | 1.0 |
| Size on disk | 10.3 GB |
| Recording type | MEG |
| Experimental modality | Resting State |
| Paradigm type | Resting-state |
| Population | Healthy |
| Source | openneuro |
| License | CC0 |
| NEMAR citations | 3.0 |
Links
- DOI: 10.18112/openneuro.ds000247.v1.0.2
- OpenNeuro: ds000247
- 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.
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