DS003458: eeg dataset, 23 subjects#

EEG: Three armed bandit gambling task

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh jcavanagh@unm.edu (2021). EEG: Three armed bandit gambling task. 10.18112/openneuro.ds003458.v1.1.0

Modality: eeg Subjects: 23 Recordings: 23 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003458

dataset = DS003458(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 = DS003458(cache_dir="./data", subject="01")

Advanced query

dataset = DS003458(
    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{ds003458,
  title = {EEG: Three armed bandit gambling task},
  author = {James F Cavanagh jcavanagh@unm.edu},
  doi = {10.18112/openneuro.ds003458.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003458.v1.1.0},
}

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

Dataset Information#

Dataset ID	`DS003458`
Title	EEG: Three armed bandit gambling task
Author (year)	`Cavanagh2021_Three`
Canonical	—
Importable as	`DS003458`, `Cavanagh2021_Three`
Year	2021
Authors	James F Cavanagh jcavanagh@unm.edu
License	CC0
Citation / DOI	10.18112/openneuro.ds003458.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: 23
Recordings: 23
Tasks: 1

Channels & sampling rate

Channels: 66 (19), 64 (4)
Sampling rate (Hz): 500.0
Duration (hours): 10.447388888888888

Electrode Layout#

Electrode layout — EEG · 63 sensors — 63 channels

Dataset Statistics#

Age distribution (n=23, range 18–24 yr)

1520

Sex distribution

23

Other Total: 23

Channel counts (ch)

6466

Sampling frequencies: 500.0 Hz (n=23 recordings)

Total recording duration: 10 h 26 min

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

Signal Preview#

Live trace viewer — sub-021 · 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.

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS003458 class to access this dataset programmatically.

class eegdash.dataset.DS003458(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG: Three armed bandit gambling task

Study:: ds003458 (OpenNeuro)
Author (year):: Cavanagh2021_Three
Canonical:: —

Also importable as: DS003458, Cavanagh2021_Three.

Modality: eeg. 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

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/ds003458 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003458 DOI: https://doi.org/10.18112/openneuro.ds003458.v1.1.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS003458
>>> dataset = DS003458(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)