EEGDash/ds004123

BCIT Traffic Complexity

Dataset ID: ds004123

Touryan2022_BCIT_Traffic

Canonical aliases: BCIT_Traffic_Complexity

At a glance: EEG · Visual attention · healthy · 29 subjects · 30 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="ds004123", 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 BCIT_Traffic_Complexity
ds = BCIT_Traffic_Complexity(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/ds004123")

Dataset metadata

Subjects	29
Recordings	30
Tasks (count)	1
Channels	74 (×30)
Sampling rate (Hz)	1024 (×30)
Size on disk	17.5 GB
Recording type	EEG
Experimental modality	Visual
Paradigm type	Attention
Population	Healthy
Source	openneuro
License	CC0
NEMAR citations	0.0

Links

• DOI: 10.18112/openneuro.ds004123.v1.0.0
• OpenNeuro: ds004123
• Browse 700+ datasets: EEGDash catalog
• Docs: https://eegdash.org
• Code: https://github.com/eegdash/EEGDash

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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.