Datasets:

EEGDash
/

ds005363

pretty_name: Object recognition in healthy aging (ORHA) - EEG
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - perception
size_categories:
  - n<1K
task_categories:
  - other

Object recognition in healthy aging (ORHA) - EEG

Dataset ID: ds005363

Haupt2024_Object

Canonical aliases: ORHA

At a glance: EEG · Visual perception · healthy · 43 subjects · 43 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="ds005363", 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 ORHA
ds = ORHA(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/ds005363")

Dataset metadata


Subjects	43
Recordings	43
Tasks (count)	1
Channels	64 (×43)
Sampling rate (Hz)	1000 (×43)
Total duration (h)	43.1
Size on disk	17.7 GB
Recording type	EEG
Experimental modality	Visual
Paradigm type	Perception
Population	Healthy
Source	openneuro
License	CC0
NEMAR citations	1.0

Object recognition in healthy aging (ORHA) - EEG

Load this dataset

Dataset metadata

Links