DS004917#

Probability Decision-making Task with ambiguity

Access recordings and metadata through EEGDash.

Citation: Alejandra Figueroa-Vargas, Gabriela Valdebenito-Oyarzo, María Paz Martínez-Molina, Francisco Zamorano, Pablo Billeke (2024). Probability Decision-making Task with ambiguity. 10.18112/openneuro.ds004917.v1.0.1

Modality: eeg Subjects: 53 Recordings: 874 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004917

dataset = DS004917(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS004917(cache_dir="./data", subject="01")

Advanced query

dataset = DS004917(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds004917,
  title = {Probability Decision-making Task with ambiguity},
  author = {Alejandra Figueroa-Vargas and Gabriela Valdebenito-Oyarzo and María Paz Martínez-Molina and Francisco Zamorano and Pablo Billeke},
  doi = {10.18112/openneuro.ds004917.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004917.v1.0.1},
}

About This Dataset#

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 (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.

Dataset Information#

Dataset ID	`DS004917`
Title	Probability Decision-making Task with ambiguity
Year	2024
Authors	Alejandra Figueroa-Vargas, Gabriela Valdebenito-Oyarzo, María Paz Martínez-Molina, Francisco Zamorano, Pablo Billeke
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004917.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 53
Recordings: 874
Tasks: 1

Channels & sampling rate

Channels: 66
Sampling rate (Hz): 5000.0
Duration (hours): 0.0

API Reference#

Use the DS004917 class to access this dataset programmatically.

class eegdash.dataset.DS004917(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004917. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004917 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004917

Examples

>>> from eegdash.dataset import DS004917
>>> dataset = DS004917(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None