DS004532: eeg dataset, 110 subjects#
EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge
Citation: James F Cavanagh, Michael J Frank (20). EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge. 10.18112/openneuro.ds004532.v1.2.0
110-participant EEG dataset — EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004532
dataset = DS004532(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004532(cache_dir="./data", subject="01")
Advanced query
dataset = DS004532(
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{ds004532,
title = {EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge},
author = {James F Cavanagh and Michael J Frank},
doi = {10.18112/openneuro.ds004532.v1.2.0},
url = {https://doi.org/10.18112/openneuro.ds004532.v1.2.0},
}
About This Dataset#
Probabilistic Selection Task. Unpublished! Same sample as this published study: 10.1038/ncomms6394.
Study 1: 80 healthy participants + 5 placebo session from a pilot of the drug study. Total n=85. But Subj 173 might have bad EEG.
Study 2: 30 healthy participants (3 dropout) in a double-blind drug study. Total n=27. Drug was Cabergoline 1.25 mg.
If you look in the code folder at the .xls sheet, you’ll see that subjects had different initial IDs. Study 1 subjects had subject IDs 101-180 plus the 5 placebo runs from an early test of ultra-low-dose cabergoline: these pilot runs were subject # 301/401 | 305/405. Study 2 subjects had IDs 306/406 | 335/435. Why the odd ranges for the drug study? Glad you asked. The dual numbers were for session: 300s were first session, 400s were second session. The last two digits were subject ID. (here with the benefit of BIDS formatting we have simply put them in as session 1 and session 2 with unique sub-#, which is BETTER).
For example. Joe Smith would have been 305 on visit 1, then 405 on visit 2. Jane Henderson would have been 306 on visit 1, then 406 on visit 2. Whatever visit got cab or placebo is indicated in the .xls sheet as well as on the Sess1_Drug and Sess2_Drug columns in the main .tsv file. Task included in Matlab programming language. Data collected circa 2012-2013 in Laboratory for Neural Computation & Cognition at Brown. Check the .xls sheet under code folder for more meta data. A few old analysis scripts are included. - James F Cavanagh 02/15/2021 UPDATES: 1) Uploaded a .json sidecar developed by EEGLab for NEMAR indexing: task-PST_events.json 2) Since this was updated, I had to erase each subject’s *_events.json files. 3) Note that the Reward and Penalty feedback labels (‘FB: 0’ and ‘FB: +1’) are incorrect here. The actual feedback was ‘Correct!’ or ‘Incorrect.’ I’m just going to leave those as-is in the files since it doesn’t change too much. Run the task (under /stimuli) to see what the feedbacks look like. 4) there was a bug in the original task description that indicated this as ‘Simon Conflict’. This is not that task. This is a Probabilistic Selection Task. These should have been changed to PST, but if you see SimonConflict just realize that was an original mis-label.
Cohort#
Dataset Statistics#
Age distribution by gender (n=110, range 18–30 yr, mean 20.4 yr)
Sex composition
Channel counts: 64 ch (n=137 recordings)
Sampling frequencies: 500.0 Hz (n=137 recordings)
Total recording duration: 49 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · ses-01 · task-PST
Showing one representative recording out of
110 subjects and 137 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 |
EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
James F Cavanagh, Michael J Frank |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004532,
title = {EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge},
author = {James F Cavanagh and Michael J Frank},
doi = {10.18112/openneuro.ds004532.v1.2.0},
url = {https://doi.org/10.18112/openneuro.ds004532.v1.2.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004532 · Cavanagh2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004532(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge
- Study:
ds004532(OpenNeuro)- Author (year):
Cavanagh2023- Canonical:
—
Also importable as:
DS004532,Cavanagh2023.Modality:
eeg. Subjects: 110; recordings: 137; 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/ds004532 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004532 DOI: https://doi.org/10.18112/openneuro.ds004532.v1.2.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS004532 >>> dataset = DS004532(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/ds004532").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 ds004532 to reproduce the tutorial on this dataset.
Citation
James F Cavanagh, Michael J Frank (20). EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge. 10.18112/openneuro.ds004532.v1.2.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004532.v1.2.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset