DS004602: eeg dataset, 182 subjects#

Registered Replication Report of ERN/Pe Psychometrics

Access recordings and metadata through EEGDash.

Citation: Peter E Clayson, Michael J Larson (2023). Registered Replication Report of ERN/Pe Psychometrics. 10.18112/openneuro.ds004602.v1.0.1

Modality: eeg Subjects: 182 Recordings: 546 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004602

dataset = DS004602(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 = DS004602(cache_dir="./data", subject="01")

Advanced query

dataset = DS004602(
    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{ds004602,
  title = {Registered Replication Report of ERN/Pe Psychometrics},
  author = {Peter E Clayson and Michael J Larson},
  doi = {10.18112/openneuro.ds004602.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004602.v1.0.1},
}

About This Dataset#

This dataset supports a registered replication report that is described at https://osf.io/8cbua/. Scripts used for data processing are posted there. Abstract Intact cognitive control is critical for goal-directed behavior and is widely studied in healthy and clinical populations using the error-related negativity (ERN). A common assumption in such studies is that ERNs recorded during different experimental paradigms reflect the same construct or functionally equivalent processes and that ERN is functionally distinct from other error-monitoring event-related potentials (ERPs; error positivity [Pe]), other neurophysiological indices of cognitive control (N2), and even other indices unrelated to cognitive control (visual N1). The present registered report represents a replication-plus-extension study of the psychometric validity of cognitive control ERPs (Riesel et al., 2013, Biological Psychology) and evaluated the convergent and divergent validity of ERN, Pe, N2, and visual N1 recorded during three paradigms (flanker, Stroop, Go/no-go). Data from 182 participants were collected from two study sites, and ERP psychometric reliability and validity were evaluated. Findings supported convergent and divergent validity of ERN, Pe, and delta-Pe (error minus correct)—these ERPs correlated more with themselves across tasks than with other ERPs measured during the same task. Convergent validity of delta-ERN was not replicated, despite high internal consistency. ERN was strongly correlated with N2 at levels similar or higher than those in support of convergent validity for other ERPs, and the present study failed to provide evidence of divergent validity for ERN and Pe from N2 or the theoretically unrelated N1. Present findings underscore the importance of considering the psychometric validity of ERPs as it provides a foundation for interpreting and comparing ERPs across different tasks and studies.

Dataset Information#

Dataset ID	`DS004602`
Title	Registered Replication Report of ERN/Pe Psychometrics
Author (year)	`Clayson2023_Registered`
Canonical	—
Importable as	`DS004602`, `Clayson2023_Registered`
Year	2023
Authors	Peter E Clayson, Michael J Larson
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004602.v1.0.1
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: 182
Recordings: 546
Tasks: 3

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 500.0 (501), 250.0 (45)
Duration (hours): 87.17373944444445

Electrode Layout#

Electrode layout — EEG · 129 sensors — 129 channels

Dataset Statistics#

Age distribution (n=182, range 18–32 yr)

15202530

Sex distribution

117

65

Female Male Total: 182

Channel counts: 129 ch (n=546 recordings)

Sampling frequencies (Hz)

250500

Total recording duration: 87 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS004602 class to access this dataset programmatically.

class eegdash.dataset.DS004602(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Registered Replication Report of ERN/Pe Psychometrics

Study:: ds004602 (OpenNeuro)
Author (year):: Clayson2023_Registered
Canonical:: —

Also importable as: DS004602, Clayson2023_Registered.

Modality: eeg. Subjects: 182; recordings: 546; 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/ds004602 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004602 DOI: https://doi.org/10.18112/openneuro.ds004602.v1.0.1 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS004602
>>> dataset = DS004602(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)