Datasets:

EEGDash
/

ds004657

pretty_name: Driving with Autonomous Aids
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - decision-making
size_categories:
  - n<1K
task_categories:
  - other

Driving with Autonomous Aids

Dataset ID: ds004657

Metcalfe2023_Driving

Canonical aliases: TX20

At a glance: EEG · Visual decision-making · healthy · 24 subjects · 119 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="ds004657", 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 TX20
ds = TX20(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/ds004657")

Dataset metadata


Subjects	24
Recordings	119
Tasks (count)	1
Channels	74 (×119)
Sampling rate (Hz)	1024 (×111), 8192 (×8)
Total duration (h)	27.2
Size on disk	43.1 GB
Recording type	EEG
Experimental modality	Visual
Paradigm type	Decision-making
Population	Healthy
Source	openneuro
License	CC0
NEMAR citations	1.0

Driving with Autonomous Aids

Load this dataset

Dataset metadata

Links