EEGDash/ds004520

Changes in behavioral priority influence the accessibility of working memory content - Experiment 2

Dataset ID: ds004520

Ester2023_Changes

Canonical aliases: Ester2024_E2

At a glance: EEG · Visual memory · healthy · 33 subjects · 33 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="ds004520", 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 Ester2024_E2
ds = Ester2024_E2(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/ds004520")

Dataset metadata

Subjects	33
Recordings	33
Tasks (count)	1
Channels	62 (×33)
Sampling rate (Hz)	250 (×33)
Total duration (h)	0.1
Size on disk	10.4 GB
Recording type	EEG
Experimental modality	Visual
Paradigm type	Memory
Population	Healthy
Source	openneuro
License	CC0
NEMAR citations	3.0

Links

• DOI: 10.18112/openneuro.ds004520.v1.0.1
• OpenNeuro: ds004520
• 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.