DS004917: eeg dataset, 24 subjects#
Probability Decision-making Task with ambiguity
Citation: Alejandra Figueroa-Vargas, Gabriela Valdebenito-Oyarzo, María Paz Martínez-Molina, Francisco Zamorano, Pablo Billeke (—). Probability Decision-making Task with ambiguity. 10.18112/openneuro.ds004917.v1.0.1
24-participant EEG dataset — Probability Decision-making Task with ambiguity.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=24, range 18–31 yr, mean 24.1 yr)
Sex composition
Channel counts: 66 ch (n=24 recordings)
Sampling frequencies: 5000.0 Hz (n=24 recordings)
Total recording duration: 14 h 34 min
Signal · Electrodes & live trace#
Live trace viewer — sub-14 · task-pdm
Showing one representative recording out of
24 subjects and 24 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 62 sensors — 62 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Probability Decision-making Task with ambiguity |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Alejandra Figueroa-Vargas, Gabriela Valdebenito-Oyarzo, María Paz Martínez-Molina, Francisco Zamorano, Pablo Billeke |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004917 · FigueroaVargas2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004917(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Probability Decision-making Task with ambiguity
- Study:
ds004917(OpenNeuro)- Author (year):
FigueroaVargas2024- Canonical:
—
Also importable as:
DS004917,FigueroaVargas2024.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds004917.v1.0.1 NEMAR citation count: 0
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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004917").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds004917 to reproduce the tutorial on this dataset.
Citation
Alejandra Figueroa-Vargas, Gabriela Valdebenito-Oyarzo, María Paz Martínez-Molina, Francisco Zamorano, Pablo Billeke (n.d.). Probability Decision-making Task with ambiguity. 10.18112/openneuro.ds004917.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004917.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset