DS004579: eeg dataset, 139 subjects#
Interval Timing Task
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 (—). Interval Timing Task. 10.18112/openneuro.ds004579.v1.0.0
139-participant EEG dataset — Interval Timing Task.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Age distribution (n=139, range 48–86 yr, mean 69.4 yr · sex per subject not reported)
Channel counts (ch)
Sampling frequencies: 500.0 Hz (n=139 recordings)
Total recording duration: 55 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-IntervalTiming
Showing one representative recording out of
139 subjects and 139 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.
Electrode layout — EEG · 63 sensors — 63 channels
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
Manifest#
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.
Full dataset metadata table
Dataset ID |
|
Title |
Interval Timing Task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
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 |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004579 · Singh2023_Interval_Timingeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004579(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Interval Timing Task
- Study:
ds004579(OpenNeuro)- Author (year):
Singh2023_Interval_Timing- Canonical:
—
Also importable as:
DS004579,Singh2023_Interval_Timing.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds004579.v1.0.0 NEMAR citation count: 1
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: 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)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004579").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds004579 to reproduce the tutorial on this dataset.
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, … (n.d.). Interval Timing Task. 10.18112/openneuro.ds004579.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004579.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset