EEGDash/ds004830

Spatial Attention Decoding using fNIRS During Complex Scene Analysis

Dataset ID: ds004830

Ning2023

Canonical aliases: Ning2024

At a glance: FNIRS · Visual attention · healthy · 12 subjects · 14 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="ds004830", 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 Ning2024
ds = Ning2024(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/ds004830")

Dataset metadata

Subjects	12
Recordings	14
Tasks (count)	1
Channels	72 (×27), 84 (×6)
Sampling rate (Hz)	50 (×32), 50.00000000000001 (×1)
Size on disk	1.2 GB
Recording type	FNIRS
Experimental modality	Visual
Paradigm type	Attention
Population	Healthy
Source	openneuro
License	CC0

Links

• DOI: 10.18112/openneuro.ds004830.v2.0.0
• OpenNeuro: ds004830
• 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.