EEGDash/ds005545

Auditory naming

Dataset ID: ds005545

Kanno2024

Canonical aliases: Kanno2025

At a glance: IEEG · Auditory other · surgery · 106 subjects · 336 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="ds005545", 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 Kanno2025
ds = Kanno2025(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/ds005545")

Dataset metadata

Subjects	106
Recordings	336
Tasks (count)	1
Channels	128 (×237), 138 (×14), 136 (×11), 134 (×11), 140 (×8), 112 (×6), 110 (×6), 156 (×5), 142 (×5), 150 (×5), 164 (×4), 132 (×4), 144 (×4), 148 (×4), 118 (×3), 116 (×3), 84 (×3), 96 (×3)
Sampling rate (Hz)	1000 (×336)
Total duration (h)	117.6
Size on disk	40.0 GB
Recording type	IEEG
Experimental modality	Auditory
Paradigm type	Other
Population	Surgery
Source	openneuro
License	CC0
NEMAR citations	0.0

Links

• DOI: 10.18112/openneuro.ds005545.v1.0.3
• OpenNeuro: ds005545
• 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.