Datasets:

EEGDash
/

ds004830

pretty_name: Spatial Attention Decoding using fNIRS During Complex Scene Analysis
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - attention
size_categories:
  - n<1K
task_categories:
  - other

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

Spatial Attention Decoding using fNIRS During Complex Scene Analysis

Load this dataset

Dataset metadata

Links