ON003458: eeg dataset, 23 subjects#
EEG: Three armed bandit gambling task
Access recordings and metadata through EEGDash.
Citation: James F Cavanagh jcavanagh@unm.edu (20). EEG: Three armed bandit gambling task. 10.82901/nemar.on003458
Modality: eeg Subjects: 23 Recordings: 23 License: CC0 Source: nemar
Metadata: Complete (100%)
23-participant EEG dataset — EEG: Three armed bandit gambling task.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON003458
dataset = ON003458(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON003458(cache_dir="./data", subject="01")
Advanced query
dataset = ON003458(
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{on003458,
title = {EEG: Three armed bandit gambling task},
author = {James F Cavanagh jcavanagh@unm.edu},
doi = {10.82901/nemar.on003458},
url = {https://doi.org/10.82901/nemar.on003458},
}
About This Dataset#
Healthy control college students. 23 subjects completed the 3-armed bandit task with oscillating probabilities. For example, the ‘blue’ stim would slowly move from 20% reinforcing to 90% then back to 20 over many trials. The other ‘red’ and ‘green’ stims would move similarly, but in different phase. See Fig 1 of the paper. This makes the task great for investigating reward processing & reward prediction error in the service of novel task set generation.
Task included in Matlab programming language.
Data collected in 2014 in the Cognitive Rhythms and Computation Lab, University of New Mexico. I also collected Corrugator EMG (may be labeled EKG) and Skin Conductance on most people. But quality was dubious so I never did much with it. Check .xls sheet under code folder.
Some pre-processing scripts are included in code folder as well. - James F Cavanagh 01/04/2021
Cohort#
Dataset Statistics#
Age distribution by gender (n=23, range 18–24 yr, mean 20.0 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 500.0 Hz (n=23 recordings)
Total recording duration: 10 h 26 min
Signal · Electrodes & live trace#
Live trace viewer — sub-001 · task-ThreeArmedBandit
Showing one representative recording out of
23 subjects and 23 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 · 63 sensors — 63 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 |
EEG: Three armed bandit gambling task |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
James F Cavanagh jcavanagh@unm.edu |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on003458,
title = {EEG: Three armed bandit gambling task},
author = {James F Cavanagh jcavanagh@unm.edu},
doi = {10.82901/nemar.on003458},
url = {https://doi.org/10.82901/nemar.on003458},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON003458(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: Three armed bandit gambling task
- Study:
on003458(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON003458.Modality:
eeg; Subject type:Unknown. Subjects: 23; recordings: 23; 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/on003458 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on003458 DOI: https://doi.org/10.82901/nemar.on003458
Examples
>>> from eegdash.dataset import ON003458 >>> dataset = ON003458(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.pytorch
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 on003458 to reproduce the tutorial on this dataset.
Citation
James F Cavanagh jcavanagh@unm.edu (20). EEG: Three armed bandit gambling task. 10.82901/nemar.on003458
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on003458.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset