dataset_id stringclasses 1
value | title stringclasses 1
value | source stringclasses 1
value | source_url stringclasses 1
value | doi stringclasses 1
value | license stringclasses 1
value | loader dict | catalog stringclasses 1
value | generated_by stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|
ds000248 | MNE-Sample-Data | openneuro | https://openneuro.org/datasets/ds000248 | 10.18112/openneuro.ds000248.v1.2.4 | CC0 | {
"library": "eegdash",
"class": "EEGDashDataset",
"kwargs": {
"dataset": "ds000248"
}
} | https://huggingface.co/spaces/EEGDash/catalog | huggingface-space/scripts/push_metadata_stubs.py |
MNE-Sample-Data
Dataset ID: ds000248
Gramfort2018
Canonical aliases: MNE_Sample_Data
At a glance: MEG · Multisensory attention · healthy · 2 subjects · 3 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="ds000248", 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 MNE_Sample_Data
ds = MNE_Sample_Data(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/ds000248")
Dataset metadata
| Subjects | 2 |
| Recordings | 3 |
| Tasks (count) | 2 |
| Channels | 376 (×1), 315 (×1) |
| Sampling rate (Hz) | 600.614990234375 (×2) |
| Total duration (h) | 0.1 |
| Size on disk | 177.6 MB |
| Recording type | MEG |
| Experimental modality | Multisensory |
| Paradigm type | Attention |
| Population | Healthy |
| Source | openneuro |
| License | CC0 |
| NEMAR citations | 3.0 |
Links
- DOI: 10.18112/openneuro.ds000248.v1.2.4
- OpenNeuro: ds000248
- 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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