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 |
|---|---|---|---|---|---|---|---|---|
nm000229 | Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing | nemar | https://openneuro.org/datasets/nm000229 | 10.1038/s41597-023-02752-5 | CC0 | {
"library": "eegdash",
"class": "EEGDashDataset",
"kwargs": {
"dataset": "nm000229"
}
} | https://huggingface.co/spaces/EEGDash/catalog | huggingface-space/scripts/push_metadata_stubs.py |
Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing
Dataset ID: nm000229
Gwilliams2023
Canonical aliases: MASC_MEG · MEG_MASC
At a glance: EEG · 29 subjects · 1360 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="nm000229", 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 MASC_MEG
ds = MASC_MEG(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/nm000229")
Dataset metadata
| Subjects | 29 |
| Recordings | 1360 |
| Tasks (count) | 79 |
| Channels | 208 (×196) |
| Sampling rate (Hz) | 1000 (×196) |
| Size on disk | Unknown |
| Recording type | EEG |
| Source | nemar |
| License | CC0 |
Links
- DOI: 10.1038/s41597-023-02752-5
- NEMAR: nm000229
- 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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