DS003506: eeg dataset, 56 subjects#
EEG: Reinforcement Learning in Parkinson’s
Citation: James F Cavanagh, Darin Brown (20). EEG: Reinforcement Learning in Parkinson’s. 10.18112/openneuro.ds003506.v1.1.0
56-participant EEG dataset — EEG: Reinforcement Learning in Parkinson's.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003506
dataset = DS003506(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003506(cache_dir="./data", subject="01")
Advanced query
dataset = DS003506(
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{ds003506,
title = {EEG: Reinforcement Learning in Parkinson's},
author = {James F Cavanagh and Darin Brown},
doi = {10.18112/openneuro.ds003506.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds003506.v1.1.0},
}
About This Dataset#
Reinforcement learning task with 28 Parkinson patients and 28 matched controls. Task with volitional and instucted choices. Task adapted from here: https://doi.org/10.1016/j.neuron.2014.06.035. Beh data first published here: 10.1016/j.cortex.2017.02.021. EEG published here: 10.1016/j.brainres.2019.146541. 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. Some Matlab analytic scripts are included, but I didnt ensure that these are complete. Also behavioral files from the task, which contain more trial-specific information than the triggers.
James F Cavanagh 02/05/2021
Cohort#
Dataset Statistics#
Age distribution by gender (n=56, range 48–84 yr, mean 69.5 yr)
Sex composition
Channel counts: 67 ch (n=84 recordings)
Sampling frequencies: 500.0 Hz (n=84 recordings)
Total recording duration: 35 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · ses-02 · task-ReinforcementLearning
Showing one representative recording out of
56 subjects and 84 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 · 64 sensors — 64 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 |
EEG: Reinforcement Learning in Parkinson’s |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
James F Cavanagh, Darin Brown |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003506,
title = {EEG: Reinforcement Learning in Parkinson's},
author = {James F Cavanagh and Darin Brown},
doi = {10.18112/openneuro.ds003506.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds003506.v1.1.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003506 · Cavanagh2021_Reinforcementeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003506(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: Reinforcement Learning in Parkinson’s
- Study:
ds003506(OpenNeuro)- Author (year):
Cavanagh2021_Reinforcement- Canonical:
—
Also importable as:
DS003506,Cavanagh2021_Reinforcement.Modality:
eeg. 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
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/ds003506 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003506 DOI: https://doi.org/10.18112/openneuro.ds003506.v1.1.0 NEMAR citation count: 4
Examples
>>> from eegdash.dataset import DS003506 >>> dataset = DS003506(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/ds003506").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 ds003506 to reproduce the tutorial on this dataset.
Citation
James F Cavanagh, Darin Brown (20). EEG: Reinforcement Learning in Parkinson's. 10.18112/openneuro.ds003506.v1.1.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003506.v1.1.0.
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+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset