DS004860: eeg dataset, 31 subjects#
Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study
Citation: Flora Schwartz, Radouane El-Yagoubi, Julie Cayron, Pierre-Vincent Paubel, Bastien Tremoliere (—). Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study. 10.18112/openneuro.ds004860.v1.0.0
31-participant EEG dataset — Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=31, range 19–45 yr, mean 22.3 yr)
Sex composition
Channel counts: 36 ch (n=31 recordings)
Sampling frequencies (Hz)
Total recording duration: 16 h 21 min
Signal · Electrodes & live trace#
Live trace viewer — sub-130 · task-HarmN400
Showing one representative recording out of
31 subjects and 31 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Flora Schwartz, Radouane El-Yagoubi, Julie Cayron, Pierre-Vincent Paubel, Bastien Tremoliere |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004860 · Schwartz2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004860(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
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/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)
- 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/ds004860").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 ds004860 to reproduce the tutorial on this dataset.
Citation
Flora Schwartz, Radouane El-Yagoubi, Julie Cayron, Pierre-Vincent Paubel, Bastien Tremoliere (n.d.). Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study. 10.18112/openneuro.ds004860.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.ds004860.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset