Datasets:

EEGDash
/

ds007427

pretty_name: >-
  Comprehensive methodology for sample enrichment in EEG biomarker studies for
  Alzheimer’s risk classification
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - resting-state
  - clinical-intervention
  - dementia
size_categories:
  - n<1K
task_categories:
  - other

Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification

Dataset ID: ds007427

Isaza2026_Comprehensive

Canonical aliases: HenaoIsaza2026

At a glance: EEG · Resting State clinical/intervention · dementia · 44 subjects · 44 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="ds007427", 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 HenaoIsaza2026
ds = HenaoIsaza2026(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/ds007427")

Dataset metadata


Subjects	44
Recordings	44
Tasks (count)	1
Channels	60 (×44)
Sampling rate (Hz)	1000 (×44)
Total duration (h)	3.9
Size on disk	3.1 GB
Recording type	EEG
Experimental modality	Resting State
Paradigm type	Clinical/Intervention
Population	Dementia
Source	openneuro
License	CC0

Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification

Load this dataset

Dataset metadata

Links