DS003518#

EEG: Simon Conflict w/ Reinforcement + Cabergoline Challenge

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh, Michael J Frank (2021). EEG: Simon Conflict w/ Reinforcement + Cabergoline Challenge. 10.18112/openneuro.ds003518.v1.1.0

Modality: eeg Subjects: 110 Recordings: 1265 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003518

dataset = DS003518(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 = DS003518(cache_dir="./data", subject="01")

Advanced query

dataset = DS003518(
    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{ds003518,
  title = {EEG: Simon Conflict w/ Reinforcement + Cabergoline Challenge},
  author = {James F Cavanagh and Michael J Frank},
  doi = {10.18112/openneuro.ds003518.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003518.v1.1.0},
}

About This Dataset#

Simon conflict task with cost of conflict reinforcement manipulation. Study 1: 80 healthy participants (2 removed) + 5 placebo session from a pilot of the drug study. Total n=83. Study 2: 30 healthy participants (3 dropout) in a double-blind drug study. Total n=27. Drug was Cabergoline 1.25 mg. Study 1 subjects had IDs 101-180 and the 5 placebo were 301/401 - 305/405. Study 2 subjects had IDs 305/405 - 330/430. The dual numbers were for session: 300s were first session, 400s were second session. Here we have simply put them in as session 1 and session 2. So Joe Smith would have been 305 on visit 1, then 405 on visit 2. If he got cab first we indicated that in the Sess1_Drug column. EEG published here: 10.1038/ncomms6394. Task included in Matlab programming language. Data collected circa 2012-2013 in Laboratory for Neural Computation & Cognition at Brown. Check the .xls sheet under code folder for more meta data. Triggers are complicated. See CC_Triggers.mat under code folder. A few old analysis scripts are included. - James F Cavanagh 02/15/2021

Dataset Information#

Dataset ID	`DS003518`
Title	EEG: Simon Conflict w/ Reinforcement + Cabergoline Challenge
Year	2021
Authors	James F Cavanagh, Michael J Frank
License	CC0
Citation / DOI	10.18112/openneuro.ds003518.v1.1.0
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: 110
Recordings: 1265
Tasks: 1

Channels & sampling rate

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

API Reference#

Use the DS003518 class to access this dataset programmatically.

class eegdash.dataset.DS003518(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003518. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 110; recordings: 137; 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/ds003518 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003518

Examples

>>> from eegdash.dataset import DS003518
>>> dataset = DS003518(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