DS004860: eeg dataset, 31 subjects#

Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study

Access recordings and metadata through EEGDash.

Citation: Flora Schwartz, Radouane El-Yagoubi, Julie Cayron, Pierre-Vincent Paubel, Bastien Tremoliere (2023). Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study. 10.18112/openneuro.ds004860.v1.0.0

Modality: eeg Subjects: 31 Recordings: 31 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004860

dataset = DS004860(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 = DS004860(cache_dir="./data", subject="01")

Advanced query

dataset = DS004860(
    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{ds004860,
  title = {Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study},
  author = {Flora Schwartz and Radouane El-Yagoubi and Julie Cayron and Pierre-Vincent Paubel and Bastien Tremoliere},
  doi = {10.18112/openneuro.ds004860.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004860.v1.0.0},
}

About This Dataset#

EEG data collected from 31 participants as part of a research program on moral judgment. The experiment consists of a third-party moral judgment task integrated into a semantic judgment task (N400). Participants listened to moral scenarios featuring either intentional or accidental harm transgressions. The last word of the scenario appeared as text (target) and participants had to respond whether the target was congruent with the scenario they just heard by pressing a response button. The target was congruent half of the time. The agent’s intention and semantic congruency were manipulated orthogonally, leading to 4 within-subject conditions. For 20% of the moral scenarios, a moral judgment question (punishment) was presented immediately after the congruency judgment and participants indicated how much punishment the agent responsible for the moral transgression deserved using a joystick.

Dataset Information#

Dataset ID	`DS004860`
Title	Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study
Author (year)	`Schwartz2023`
Canonical	—
Importable as	`DS004860`, `Schwartz2023`
Year	2023
Authors	Flora Schwartz, Radouane El-Yagoubi, Julie Cayron, Pierre-Vincent Paubel, Bastien Tremoliere
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004860.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: 31
Recordings: 31
Tasks: 1

Channels & sampling rate

Channels: 36
Sampling rate (Hz): 512.0 (30), 2048.0
Duration (hours): Not calculated

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=31, range 19–45 yr)

152025304045

Sex distribution

25

6

Female Male Total: 31

Channel counts: 36 ch (n=31 recordings)

Sampling frequencies (Hz)

5122048

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 DS004860 class to access this dataset programmatically.

class eegdash.dataset.DS004860(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study

Study:: ds004860 (OpenNeuro)
Author (year):: Schwartz2023
Canonical:: —

Also importable as: DS004860, Schwartz2023.

Modality: eeg. Subjects: 31; recordings: 31; 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

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/ds004860 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004860 DOI: https://doi.org/10.18112/openneuro.ds004860.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004860
>>> dataset = DS004860(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)