DS004515#

EEG: Alcohol imagery reinforcement learning task with light and heavy drinker participants

Access recordings and metadata through EEGDash.

Citation: Garima Singh, James F Cavanagh (2023). EEG: Alcohol imagery reinforcement learning task with light and heavy drinker participants. 10.18112/openneuro.ds004515.v1.0.0

Modality: eeg Subjects: 54 Recordings: 1076 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004515

dataset = DS004515(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 = DS004515(cache_dir="./data", subject="01")

Advanced query

dataset = DS004515(
    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{ds004515,
  title = {EEG: Alcohol imagery reinforcement learning task with light and heavy drinker participants},
  author = {Garima Singh and James F Cavanagh},
  doi = {10.18112/openneuro.ds004515.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004515.v1.0.0},
}

About This Dataset#

Affective state reinforcement learning task in N=54 Community participants. High and low drinkers. Data collected from 2019-2021 in the CRCL at UNM. The paper [Singh, G., Campbell, E., Hogeveen, J; Witkiewitz,K., Claus, E.D., & Cavanagh, J.F. Alcohol Imagery Boosts The Reward Positivity in Heavy Drinkers] Under review at the moment. Your best bet for understanding this task would be to read that paper first. - James F Cavanagh 08/02/2022

Dataset Information#

Dataset ID	`DS004515`
Title	EEG: Alcohol imagery reinforcement learning task with light and heavy drinker participants
Year	2023
Authors	Garima Singh, James F Cavanagh
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004515.v1.0.0
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: 54
Recordings: 1076
Tasks: 1

Channels & sampling rate

Channels: 66 (54), 64 (54)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS004515 class to access this dataset programmatically.

class eegdash.dataset.DS004515(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004515. Modality: eeg; Experiment type: Affect; Subject type: Other. Subjects: 54; recordings: 54; 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/ds004515 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004515

Examples

>>> from eegdash.dataset import DS004515
>>> dataset = DS004515(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