DS004883#

Registerd Report of ERN During Three Versions of a Flanker Task

Access recordings and metadata through EEGDash.

Citation: Peter E. Clayson, Michael J. Larson (2023). Registerd Report of ERN During Three Versions of a Flanker Task. 10.18112/openneuro.ds004883.v1.0.0

Modality: eeg Subjects: 172 Recordings: 3618 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS004883`
Title	Registerd Report of ERN During Three Versions of a Flanker Task
Year	2023
Authors	Peter E. Clayson, Michael J. Larson
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004883.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 172
Recordings: 3618
Tasks: 1

Channels & sampling rate

Channels: 129 (516), 128 (516)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS004883 class to access this dataset programmatically.

class eegdash.dataset.DS004883(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004883. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None