EEGDash/ds004917

Probability Decision-making Task with ambiguity

Dataset ID: ds004917

FigueroaVargas2024

At a glance: EEG · Multisensory decision-making · healthy · 24 subjects · 24 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="ds004917", cache_dir="./cache")
print(len(ds), "recordings")

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/ds004917")

Dataset metadata

Subjects	24
Age range	18–31 yrs, mean 24.1
Recordings	24
Tasks (count)	1
Channels	66 (×24)
Sampling rate (Hz)	5000 (×24)
Total duration (h)	14.6
Size on disk	37.5 GB
Recording type	EEG
Experimental modality	Multisensory
Paradigm type	Decision-making
Population	Healthy
BIDS version	1.9.0
Source	openneuro
License	CC0
NEMAR citations	0

Tasks

• pdm

Upstream README

Verbatim from the dataset's authors — the canonical description.

Summary This dataset forms part of a study supported by the Social Neuroscience and Neuromodulation Laboratory of Universidad del Desarrollo, Chile. The full dataset is described in a submission to Scientific Data. Abstract In our daily lives, we frequently encounter decisions where the potential outcomes are unclear, leading to a state of heightened uncertainty. The complete or partial lack of knowledge regarding the probability of outcomes is called ambiguity and presents significant challenges for individuals. While recent studies have associated the level of ambiguity in decision-making with neural activity in the parietal cortex, the precise role of this brain region and its interactions with other brain regions during decision-making processes are not well known. Here, we present a comprehensive dataset detailing human decision-making under conditions of risk and ambiguity. This dataset includes data from 53 healthy volunteers aged between 18 and 31 years, consisting of structural magnetic resonance imaging (MRI: T1w, T2w, and DWI) and functional MRI (fMRI) acquired during task performance, as well as concurrent electrophysiological (EEG) recordings during inhibitory transcranial magnetic stimulation (TMS) applied over two parietal regions and the vertex. This dataset offers an opportunity to delve into the neurobiological mechanisms of decision-making in detail, highlighting the role of the parietal cortex. Additional Usage Notes

• All code related to this dataset can be found on GitHub (https://github.com/neurocics/LAN_current) and and the additional data set of study are available in the free and open repository of OSF (https://osf.io/zd3g7/) (DOI: 10.17605/OSF.IO/ZD3G7). This includes sourcedata for the scanner tasks and also stimulus presentation scripts.

People

Authors

• Alejandra Figueroa-Vargas
• Gabriela Valdebenito-Oyarzo
• María Paz Martínez-Molina
• Francisco Zamorano
• Pablo Billeke (senior)

Contact

• Alejandra Figueroa-Vargas

Funding

• ANID FONDECYT 11140535
• ANID FONDECYT 1181295
• ANID FONDECYT 1211227
• ANID FONDEQUIP EQM150076

Links

• DOI: 10.18112/openneuro.ds004917.v1.0.1
• OpenNeuro: ds004917
• Browse 700+ datasets: EEGDash catalog
• Docs: https://eegdash.org
• Code: https://github.com/eegdash/EEGDash

Provenance

• Backend: s3 — s3://openneuro.org/ds004917
• Exact size: 40,213,447,001 bytes (37.5 GB)
• Ingested: 2026-04-06
• Stats computed: 2026-04-04

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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.