Datasets:

EEGDash
/

ds004123

pretty_name: BCIT Traffic Complexity
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - attention
size_categories:
  - n<1K
task_categories:
  - other

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

BCIT Traffic Complexity

Load this dataset

Dataset metadata

Links