ON003753: eeg dataset, 25 subjects#
EEG: Probabilistic Learning with Affective Feedback: Exp #2
Access recordings and metadata through EEGDash.
Citation: Darin R. Brown, Trevor Jackson, James F Cavanagh (20). EEG: Probabilistic Learning with Affective Feedback: Exp #2. 10.82901/nemar.on003753
Modality: eeg Subjects: 25 Recordings: 25 License: CC0 Source: nemar
Metadata: Complete (100%)
25-participant EEG dataset — EEG: Probabilistic Learning with Affective Feedback: Exp #2.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON003753
dataset = ON003753(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON003753(cache_dir="./data", subject="01")
Advanced query
dataset = ON003753(
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{on003753,
title = {EEG: Probabilistic Learning with Affective Feedback: Exp #2},
author = {Darin R. Brown and Trevor Jackson and James F Cavanagh},
doi = {10.82901/nemar.on003753},
url = {https://doi.org/10.82901/nemar.on003753},
}
About This Dataset#
RL task in N=25 college age participants. Data collected circa 2019 in the CRCL at UNM. The paper [Brown, D.R., Jackson, T.J. & Cavanagh, J.F. The Reward Positivity is sensitive to affective liking] Should be coming out in Cognitive, Affective, & Behavioral Neuroscience. THIS IS EXPERIMENT #2. Your best bet for understanding this task would be to read that paper first. Note we have since made minor adjustments to the task which really enhance the ability to resolve the RewP. I also have analytic scripts for it. If you are interetsted in running this task, contact me for the new version. - James F Cavanagh 07/02/2021
Cohort#
Dataset Statistics#
Age distribution by gender (n=25, range 18–27 yr, mean 19.6 yr)
Sex composition
Channel counts: 66 ch (n=25 recordings)
Sampling frequencies: 500.0 Hz (n=25 recordings)
Total recording duration: 10 h 6 min
Signal · Electrodes & live trace#
Live trace viewer — sub-001 · task-ProbabilisticSelection
Showing one representative recording out of
25 subjects and 25 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 Learning with Affective Feedback: Exp #2 |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Darin R. Brown, Trevor Jackson, James F Cavanagh |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on003753,
title = {EEG: Probabilistic Learning with Affective Feedback: Exp #2},
author = {Darin R. Brown and Trevor Jackson and James F Cavanagh},
doi = {10.82901/nemar.on003753},
url = {https://doi.org/10.82901/nemar.on003753},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON003753(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: Probabilistic Learning with Affective Feedback: Exp #2
- Study:
on003753(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON003753.Modality:
eeg; Subject type:Unknown. Subjects: 25; recordings: 25; 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/on003753 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on003753 DOI: https://doi.org/10.82901/nemar.on003753
Examples
>>> from eegdash.dataset import ON003753 >>> dataset = ON003753(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.pytorch
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 on003753 to reproduce the tutorial on this dataset.
Citation
Darin R. Brown, Trevor Jackson, James F Cavanagh (20). EEG: Probabilistic Learning with Affective Feedback: Exp #2. 10.82901/nemar.on003753
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on003753.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset