DS003987: eeg dataset, 23 subjects#
EEG: Amphetamine trials 5CCPT and Probabilistic Learning
Citation: James F Cavanagh, Greg Light, Neal Swerdlow, Jonathan Brigman, Jared Young (20). EEG: Amphetamine trials 5CCPT and Probabilistic Learning. 10.18112/openneuro.ds003987.v1.0.0
23-participant EEG dataset — EEG: Amphetamine trials 5CCPT and Probabilistic Learning.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003987
dataset = DS003987(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003987(cache_dir="./data", subject="01")
Advanced query
dataset = DS003987(
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{ds003987,
title = {EEG: Amphetamine trials 5CCPT and Probabilistic Learning},
author = {James F Cavanagh and Greg Light and Neal Swerdlow and Jonathan Brigman and Jared Young},
doi = {10.18112/openneuro.ds003987.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003987.v1.0.0},
}
About This Dataset#
Two different tasks. From: “Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms” Second phase (UH3 phase): Amphetamine trials. Data for both Bhakta et al. 5CCPT paper (humans only) and Cavanagh et al. PLT paper (humans and mice). N=23 humans. 3 drug conditions: placebo, 10mg, 20mg. N=28 mice in code folder. 4 drug condis: placebo, 0.1, 0.3, 1.0 mg/kg. EEG Triggers were odd binary recombinations that were re-translated into 0-255 in Matlab. See .m scripts and Trigger Translator.xls *********OK! LISTEN! The .bdf files were to big to import using this function. So I imported them in EEGLab, downsampled to 500 Hz, then saved them as .set files. THEN I ran the import script on these .set files. So you do not need to re-downsample in STEP1 if you run anything from the code folder. ********* Data collected circa 2016-2019 in San Diego. Data analyzed circa 2017-2021 in New Mexico. - James F Cavanagh 06/16/2021
Cohort#
Dataset Statistics#
Age distribution by gender (n=23, range 18–35 yr, mean 22.3 yr)
Sex composition
Channel counts: 71 ch (n=69 recordings)
Sampling frequencies: 500.0930232558139 Hz (n=69 recordings)
Total recording duration: 52 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · ses-02 · task-5CCPTxPSTxAmphetamine
Showing one representative recording out of
23 subjects and 69 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: Amphetamine trials 5CCPT and Probabilistic Learning |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
James F Cavanagh, Greg Light, Neal Swerdlow, Jonathan Brigman, Jared Young |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003987,
title = {EEG: Amphetamine trials 5CCPT and Probabilistic Learning},
author = {James F Cavanagh and Greg Light and Neal Swerdlow and Jonathan Brigman and Jared Young},
doi = {10.18112/openneuro.ds003987.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003987.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003987 · Cavanagh2022_Amphetamine_trials_5eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003987(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: Amphetamine trials 5CCPT and Probabilistic Learning
- Study:
ds003987(OpenNeuro)- Author (year):
Cavanagh2022_Amphetamine_trials_5- Canonical:
—
Also importable as:
DS003987,Cavanagh2022_Amphetamine_trials_5.Modality:
eeg. Subjects: 23; recordings: 69; 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/ds003987 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003987 DOI: https://doi.org/10.18112/openneuro.ds003987.v1.0.0 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS003987 >>> dataset = DS003987(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/ds003987").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 ds003987 to reproduce the tutorial on this dataset.
Citation
James F Cavanagh, Greg Light, Neal Swerdlow, Jonathan Brigman, Jared Young (20). EEG: Amphetamine trials 5CCPT and Probabilistic Learning. 10.18112/openneuro.ds003987.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.ds003987.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset