DS003509#

EEG: Simon Conflict in Parkinson’s

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh, Arun Singh, Kumar Narayanan (2021). EEG: Simon Conflict in Parkinson’s. 10.18112/openneuro.ds003509.v1.1.0

Modality: eeg Subjects: 56 Recordings: 1080 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003509

dataset = DS003509(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 = DS003509(cache_dir="./data", subject="01")

Advanced query

dataset = DS003509(
    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{ds003509,
  title = {EEG: Simon Conflict in Parkinson's},
  author = {James F Cavanagh and Arun Singh and Kumar Narayanan},
  doi = {10.18112/openneuro.ds003509.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds003509.v1.1.0},
}

About This Dataset#

Simon conflict task with cost of conflict reinforcement manipulation. 28 Parkinson patients and 28 matched controls. Task adapted from here: 10.1038/ncomms6394. Beh data first published here: 10.1016/j.cortex.2017.02.021. EEG published here: 10.1016/j.neuropsychologia.2018.05.020. PD came in twice separated by a week, either ON or OFF medication. CTL only came in once. Task included in Matlab programming language. Data collected circa 2015 in Cognitive Rhythms and Computation Lab at University of New Mexico. Subjs also had an acceleromter taped to their most tremor affected hand. X, Y, Z dimensions recorded throughout. Check the .xls sheet under code folder for more meta data. Triggers are complicated. See CC_Triggers.mat under code folder. Many analysis scripts are included; no idea how these hold up. Many are old. - James F Cavanagh 02/08/2021

Dataset Information#

Dataset ID	`DS003509`
Title	EEG: Simon Conflict in Parkinson’s
Year	2021
Authors	James F Cavanagh, Arun Singh, Kumar Narayanan
License	CC0
Citation / DOI	10.18112/openneuro.ds003509.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: 56
Recordings: 1080
Tasks: 1

Channels & sampling rate

Channels: 64 (84), 67 (84)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS003509 class to access this dataset programmatically.

class eegdash.dataset.DS003509(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003509. Modality: eeg; Experiment type: Learning; Subject type: Parkinson's. Subjects: 56; recordings: 84; 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/ds003509 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003509

Examples

>>> from eegdash.dataset import DS003509
>>> dataset = DS003509(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