DS004595: eeg dataset, 53 subjects#
EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls
Citation: Ethan Campbell, James F Cavanagh (20). EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls. 10.18112/openneuro.ds004595.v1.0.0
53-participant EEG dataset — EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004595
dataset = DS004595(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004595(cache_dir="./data", subject="01")
Advanced query
dataset = DS004595(
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{ds004595,
title = {EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls},
author = {Ethan Campbell and James F Cavanagh},
doi = {10.18112/openneuro.ds004595.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004595.v1.0.0},
}
About This Dataset#
RL task (3-armed bandit) with alcohol vs. beverage cues in N=53 Community participants. Data collected from 2019-2021 in the CRCL at UNM. The paper [Campbell, E., Singh, G., Claus, E.D., Witkiewitz,K., Costa, V.D., Hogeveen, J; & Cavanagh, J.F. Electrophysiological markers of aberrant cue-specific exploration in hazardous drinkers] Should be coming out in print soonish. Your best bet for understanding this task would be to read that paper first. For more info on triggers and outputs, see BEH_EXPLAIN.m file in code folder. - James F Cavanagh 03/06/2023
Cohort#
Dataset Statistics#
Age distribution by gender (n=53, range 18–55 yr, mean 37.9 yr)
Sex composition
Channel counts: 66 ch (n=53 recordings)
Sampling frequencies: 500.0 Hz (n=53 recordings)
Total recording duration: 17 h 4 min
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-ThreeArmedBandit
Showing one representative recording out of
53 subjects and 53 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 · 59 sensors — 59 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: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Ethan Campbell, James F Cavanagh |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004595,
title = {EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls},
author = {Ethan Campbell and James F Cavanagh},
doi = {10.18112/openneuro.ds004595.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004595.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004595 · Campbell2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004595(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls
- Study:
ds004595(OpenNeuro)- Author (year):
Campbell2023- Canonical:
—
Also importable as:
DS004595,Campbell2023.Modality:
eeg. Subjects: 53; recordings: 53; 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/ds004595 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004595 DOI: https://doi.org/10.18112/openneuro.ds004595.v1.0.0 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004595 >>> dataset = DS004595(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/ds004595").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 ds004595 to reproduce the tutorial on this dataset.
Citation
Ethan Campbell, James F Cavanagh (20). EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls. 10.18112/openneuro.ds004595.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004595.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset