EEGDash/ds007523

LPP MEG Listen

Dataset ID: ds007523

Bel2026

Canonical aliases: Dascoli2025

At a glance: MEG · Auditory perception · healthy · 58 subjects · 579 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="ds007523", 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 Dascoli2025
ds = Dascoli2025(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/ds007523")

Dataset metadata

Subjects	58
Recordings	579
Tasks (count)	1
Channels	346 (×484), 404 (×9), 400 (×9), 329 (×9), 343 (×9), 321 (×1)
Sampling rate (Hz)	1000 (×521)
Total duration (h)	94.8
Size on disk	444.8 GB
Recording type	MEG
Experimental modality	Auditory
Paradigm type	Perception
Population	Healthy
Source	openneuro
License	CC0

Links

• DOI: 10.18112/openneuro.ds007523.v1.0.0
• OpenNeuro: ds007523
• Browse 700+ datasets: EEGDash catalog
• Docs: https://eegdash.org
• Code: https://github.com/eegdash/EEGDash

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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.