DS004883: eeg dataset, 172 subjects#
Registerd Report of ERN During Three Versions of a Flanker Task
Citation: Peter E. Clayson, Michael J. Larson (—). Registerd Report of ERN During Three Versions of a Flanker Task. 10.18112/openneuro.ds004883.v1.0.0
172-participant EEG dataset — Registerd Report of ERN During Three Versions of a Flanker Task.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004883
dataset = DS004883(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004883(cache_dir="./data", subject="01")
Advanced query
dataset = DS004883(
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{ds004883,
title = {Registerd Report of ERN During Three Versions of a Flanker Task},
author = {Peter E. Clayson and Michael J. Larson},
doi = {10.18112/openneuro.ds004883.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004883.v1.0.0},
}
About This Dataset#
This study is described at https://osf.io/qt2zh/. Scripts used for data processing are posted there.
Here is the script from the manuscript that describes these data.
Error-related negativity is a widely used measure of error monitoring, and many projects are independently moving ERN recorded during a flanker task towards standardization, optimization, and eventual clinical application. However, each project uses a different version of the flanker task and tacitly assumes ERN is functionally equivalent across each version. The routine neglect of a rigorous test of this assumption undermines efforts to integrate ERN findings across tasks, optimize and standardize ERN assessment, and widely apply ERN in clinical trials. The purpose of this registered report was to determine whether ERN shows similar experimental effects (correct vs. error trials) and data quality (intraindividual variability) during three commonly-used versions of a flanker task. ERN was recorded from 172 participants during three versions of a flanker task across two study sites. ERN scores showed numerical differences between tasks, raising questions about the comparability of ERN findings across studies and tasks. Although ERN scores from all three versions of the flanker task yielded high data quality and internal consistency, one version did outperform the other two in terms of the size of experimental effects and the data quality. Exploratory analyses of the error positivity (Pe) provided tentative support for the other two versions of the task over the paradigm that appeared optimal for ERN. The present study provides a roadmap for how to statistically compare psychometric characteristics of ERP scores across paradigms and gives preliminary recommendations for flanker tasks to use for ERN- and Pe-focused studies.
Cohort#
Dataset Statistics#
Age distribution (n=172, range 18–58 yr, mean 20.2 yr · sex per subject not reported)
Sex composition
Channel counts: 129 ch (n=516 recordings)
Sampling frequencies: 500.0 Hz (n=516 recordings)
Total recording duration: 139 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-ffb
Showing one representative recording out of
172 subjects and 516 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 · 129 sensors — 129 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 |
Registerd Report of ERN During Three Versions of a Flanker Task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Peter E. Clayson, Michael J. Larson |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004883,
title = {Registerd Report of ERN During Three Versions of a Flanker Task},
author = {Peter E. Clayson and Michael J. Larson},
doi = {10.18112/openneuro.ds004883.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004883.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004883 · Clayson2023_Registerdeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004883(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Registerd Report of ERN During Three Versions of a Flanker Task
- Study:
ds004883(OpenNeuro)- Author (year):
Clayson2023_Registerd- Canonical:
—
Also importable as:
DS004883,Clayson2023_Registerd.Modality:
eeg. Subjects: 172; recordings: 516; tasks: 3.- 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/ds004883 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004883 DOI: https://doi.org/10.18112/openneuro.ds004883.v1.0.0 NEMAR citation count: 3
Examples
>>> from eegdash.dataset import DS004883 >>> dataset = DS004883(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/ds004883").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 ds004883 to reproduce the tutorial on this dataset.
Citation
Peter E. Clayson, Michael J. Larson (n.d.). Registerd Report of ERN During Three Versions of a Flanker Task. 10.18112/openneuro.ds004883.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.ds004883.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset