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ds005185
Ear-EEG Sleep Monitoring 2019 (EESM19)
openneuro
https://openneuro.org/datasets/ds005185
10.18112/openneuro.ds005185.v1.0.2
CC0
{ "library": "eegdash", "class": "EEGDashDataset", "kwargs": { "dataset": "ds005185" } }
https://huggingface.co/spaces/EEGDash/catalog
huggingface-space/scripts/push_metadata_stubs.py

Ear-EEG Sleep Monitoring 2019 (EESM19)

Dataset ID: ds005185

Mikkelsen2024_Ear_Sleep_Monitoring

Canonical aliases: EESM19

At a glance: EEG · Sleep sleep · healthy · 20 subjects · 356 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="ds005185", 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 EESM19
ds = EESM19(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/ds005185")

Dataset metadata

Subjects 20
Recordings 356
Tasks (count) 3
Channels 25 (×156)
Sampling rate (Hz) 500 (×156)
Total duration (h) 1,365.6
Size on disk 267.6 GB
Recording type EEG
Experimental modality Sleep
Paradigm type Sleep
Population Healthy
Source openneuro
License CC0

Links


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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