Datasets:

EEGDash
/

ds004519

pretty_name: >-
  Internal selective attention is delayed by competition between endogenous and
  exogenous factors
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - attention
size_categories:
  - n<1K
task_categories:
  - other

Internal selective attention is delayed by competition between endogenous and exogenous factors

Dataset ID: ds004519

Ester2023_Internal

Canonical aliases: Ester2022

At a glance: EEG · Visual attention · healthy · 40 subjects · 40 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="ds004519", 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 Ester2022
ds = Ester2022(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/ds004519")

Dataset metadata


Subjects	40
Recordings	40
Tasks (count)	1
Channels	62 (×40)
Sampling rate (Hz)	250 (×40)
Total duration (h)	0.1
Size on disk	12.6 GB
Recording type	EEG
Experimental modality	Visual
Paradigm type	Attention
Population	Healthy
Source	openneuro
License	CC0
NEMAR citations	3.0

Internal selective attention is delayed by competition between endogenous and exogenous factors

Load this dataset

Dataset metadata

Links