Datasets:

EEGDash
/

ds005545

pretty_name: Auditory naming
license: cc0-1.0
tags:
  - ieeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - auditory
  - other
  - surgery
size_categories:
  - n<1K
task_categories:
  - other

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

Auditory naming

Load this dataset

Dataset metadata

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