DS003638#

EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh, Greg Light, Neal Swerdlow, Jonathan Brigman, Jared Young (2021). EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms. 10.18112/openneuro.ds003638.v1.0.0

Modality: eeg Subjects: 57 Recordings: 404 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003638

dataset = DS003638(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 = DS003638(cache_dir="./data", subject="01")

Advanced query

dataset = DS003638(
    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{ds003638,
  title = {EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms},
  author = {James F Cavanagh and Greg Light and Neal Swerdlow and Jonathan Brigman and Jared Young},
  doi = {10.18112/openneuro.ds003638.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003638.v1.0.0},
}

About This Dataset#

Three different tasks. From: “Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms” N=57 humans. Also has mouse data in code folder. Triggers were odd binary recombinations that were re-translated into 0-255 in Matlab. See .m scripts and Trigger Translator.xlsData collected circa 2014-2016 in San Diego. Data analyzed circa 2015-2021 in New Mexico. - James F Cavanagh 04/19/2021

Dataset Information#

Dataset ID	`DS003638`
Title	EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms
Year	2021
Authors	James F Cavanagh, Greg Light, Neal Swerdlow, Jonathan Brigman, Jared Young
License	CC0
Citation / DOI	10.18112/openneuro.ds003638.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: 57
Recordings: 404
Tasks: 1

Channels & sampling rate

Channels: 72 (57), 64 (57)
Sampling rate (Hz): 512.0
Duration (hours): 0.0

API Reference#

Use the DS003638 class to access this dataset programmatically.

class eegdash.dataset.DS003638(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003638. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 57; recordings: 57; 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/ds003638 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003638

Examples

>>> from eegdash.dataset import DS003638
>>> dataset = DS003638(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