DS004579#

Interval Timing Task

Access recordings and metadata through EEGDash.

Citation: Arun Singh arun.singh@usd.edu, Rachel Cole rachel-cole@uiowa.edu, Arturo Espinoza arturo-espinoza@uiowa.edu, Jan R Wessel jan-wessel@uiowa.edu, Jim Cavanagh jcavanagh@unm.edu, Nandakumar Narayanan nandakumar-narayanan@uiowa.edu (2023). Interval Timing Task. 10.18112/openneuro.ds004579.v1.0.0

Modality: eeg Subjects: 139 Recordings: 979 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004579

dataset = DS004579(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 = DS004579(cache_dir="./data", subject="01")

Advanced query

dataset = DS004579(
    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{ds004579,
  title = {Interval Timing Task},
  author = {Arun Singh arun.singh@usd.edu and Rachel Cole rachel-cole@uiowa.edu and Arturo Espinoza arturo-espinoza@uiowa.edu and Jan R Wessel jan-wessel@uiowa.edu and Jim Cavanagh jcavanagh@unm.edu and Nandakumar Narayanan nandakumar-narayanan@uiowa.edu},
  doi = {10.18112/openneuro.ds004579.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004579.v1.0.0},
}

About This Dataset#

This experiment includes 139 subjects: 94 individuals with Parkinsons disease, and 45 controls. Subjects completed this IntervalTiming task (along with multiple other cognitive tasks) while EEG was recorded with a 64-channel BrainVision cap. This task presented black instructional text on the center of a white screen that read “Short interval” on 3-second interval trials and “Long interval” on 7-second interval trials. The researchers never communicated the actual interval durations to the patient. The instructions were displayed for 1 second, and the appearance of an image of a solid box in the center of the computer screen indicated the start of the interval. The cue was displayed on the screen for the entire trial, which lasted 6 s for 3-s intervals and 14 s for 7-s intervals. The researchers instructed participants to press the keyboard spacebar when they judged the target interval to have elapsed. Participants were directed not to count, and a distractor vowel appeared at random intervals in the screen center.

Dataset Information#

Dataset ID	`DS004579`
Title	Interval Timing Task
Year	2023
Authors	Arun Singh arun.singh@usd.edu, Rachel Cole rachel-cole@uiowa.edu, Arturo Espinoza arturo-espinoza@uiowa.edu, Jan R Wessel jan-wessel@uiowa.edu, Jim Cavanagh jcavanagh@unm.edu, Nandakumar Narayanan nandakumar-narayanan@uiowa.edu
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004579.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: 139
Recordings: 979
Tasks: 1

Channels & sampling rate

Channels: 63 (220), 64 (56), 66 (2)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS004579 class to access this dataset programmatically.

class eegdash.dataset.DS004579(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004579. Modality: eeg; Experiment type: Decision-making; Subject type: Parkinson's. Subjects: 139; recordings: 139; 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/ds004579 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004579

Examples

>>> from eegdash.dataset import DS004579
>>> dataset = DS004579(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