EEGdash›OpenNeuro›DS007688

Iss. 7688 · 44 subjects · 88 recordings · CC0

Dataset Brief · The Temporal Sequence of Party Leader Incongruence

DS007688: eeg dataset, 44 subjects#

The Temporal Sequence of Party Leader Incongruence

Citation: Gustavo Couto de Jesus, Bert N. Bakker, Gijs Schumacher, Joe Bathelt (2026). The Temporal Sequence of Party Leader Incongruence. 10.18112/openneuro.ds007688.v1.0.0

44-participant EEG dataset — The Temporal Sequence of Party Leader Incongruence.

Data & curation Gustavo Couto de Jesus · Bert N. Bakker · Gijs Schumacher · Joe Bathelt
Year 2026 · Distributed via OpenNeuro

EEG · 69 ch250 HzBIDS 1.8.02 tasks

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007688

dataset = DS007688(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 = DS007688(cache_dir="./data", subject="01")

Advanced query

dataset = DS007688(
    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{ds007688,
  title = {The Temporal Sequence of Party Leader Incongruence},
  author = {Gustavo Couto de Jesus and Bert N. Bakker and Gijs Schumacher and Joe Bathelt},
  doi = {10.18112/openneuro.ds007688.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007688.v1.0.0},
}

§ 02Study · The README

About This Dataset#

This dataset contains high-density EEG (and linked behavioral data) from a preregistered study investigating how partisans process political incongruence. The study tests the ‘Hot Cognition’ hypothesis by disentangling the temporal sequence of neural responses to party leaders (identity incongruence) and their messages (source-content incongruence).

Data was collected in the Netherlands, using supporters of the progressive GreenLeft/Labour (GL/PvdA)**party as the in-party group and the far-right Party for Freedom (PVV)** as the out-party group.

The Temporal Sequence of Party Leader Incongruence: A multitask EEG dataset

Introduction

Dataset Content

The dataset includes data from 44 healthy adult participants. It is organized according to BIDS v1.8.0.

The release includes:

View full README

The Temporal Sequence of Party Leader Incongruence: A multitask EEG dataset

Introduction

Dataset Content

The dataset includes data from 44 healthy adult participants. It is organized according to BIDS v1.8.0.

The release includes: 1. Raw EEG recordings in EDF format with BIDS-compliant JSON sidecars. Find original downsampled fif. files in sourcedata. 2. Behavioral data: Subjective ratings of agreement, surprise, and upset (Likert scales) and reaction times. You can find it in https://osf.io/xrtcy/overview 3. Event Annotations: Trial-by-trial logs including stimulus onsets, durations, trial types (congruent/incongruent), and specific stimulus file references. 4. Stimuli: A root-level /stimuli folder containing the facial cut-outs (JPG) of the political leaders used. 5. Code: Companion preprocessing and analysis scripts. 6. Derivatives: Preprocessed data 6. Metadata: Additional information

Experimental Design

The study utilized two primary tasks to isolate different stages of political information processing:

1. Just Faces Task (Identity Incongruence)

* **Goal**: To measure automatic neural responses to political social identity. * **Stimuli**: Four political leaders (Frans Timmermans & Jesse Klaver for GL/PvdA; Geert Wilders & Fleur Agema for PVV) and two unfamiliar control faces. * **Procedure**: 260 trials. Faces were presented for 700ms. * **Task**: Participants pressed a key only for unfamiliar faces to ensure attention during Just Faces task.

2. Statements & Faces Task (Source-Content Incongruence)

* **Goal**: To investigate how source identity of a political message is processed given source-content congruent and incongruent messages * **Design**: 2x2 factorial design (Face: In-party vs. Out-party; Statement: Pro-attitudinal vs. Counter-attitudinal). * **Stimuli**: 240 trials. A statement (1830–2880ms) was followed by a politician’s face (1200–1400ms). * **Issues**: Immigration, climate change, EU expansion, gender equality, vegetarianism, and cultural integration. * **Task**: In ~18% of trials, participants rated their agreement, surprise, or upset.

Data Acquisition

* **EEG System**: 64-channel BioSemi ActiveTwo system. * **Electrodes**: International 10-20 layout, including EOG, ECG, and mastoid leads. * **Reference**: Recorded with CMS/DRL; re-referenced to average reference during preprocessing. * **Filtering**: Data were band-pass filtered offline (0.5–40 Hz). * **Software**: Stimuli were presented and analysed using PsychoPy and MNE-Python.

Technical Validation

* **Behavioral**: Analysis confirmed strong in-party favoritism. Participants agreed more with in-party leaders and felt greater surprise/upset when in-party leaders were paired with counter-attitudinal statements. * **Neural (ERP)**: Cluster-based permutation tests revealed an enhanced early posterior positivity (Visual P2) for out-party stimuli emerging as early as 148ms, suggesting rapid, pre-conscious detection of political opponents.

Data Quality Notes

* **Participant Exclusions**:

Just Faces (H1): n=37 included after excluding for EEG noise/bad channels.
Statements & Faces (H3): n=36 included due to technical issues in trial recording for some participants.

* **Bad Channels**: Frequently interpolated channels included AF7, F7, FT8, FC5, and C2. * **Demographics**: The sample consists primarily of young, progressive-leaning university students (Mean age = 21.4).

Usage Recommendations

* **Citing this dataset**: Please search associated the preprint or paper with the following name:

Couto de Jesus, G., Bakker, B. N., Schumacher, G., & Bathelt, J. (2026). The Temporal Sequence of Party Leader Incongruence: a data-driven ERP study.

* **Running its code**

The python scripts stored in code were built to analyse downsampled fif. files (see sourcedata).

Acknowledgements

Funding was provided by: * **IP-PAD**: European Union’s Horizon Europe MSCA Doctoral Networks (Grant No. 101072992). * **POLEMIC**: European Research Council (ERC) (Grant No. 759079). * **NWO**: Talent Programme VIDI (Project No. VI.Vidi.211.055).

Special thanks to Anna Mae van Dooren, Lois van Petegem, and Rayna Akil for data collection assistance.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=44, range 19–38 yr, mean 21.4 yr)

15202535

Female · 38Male · 6

Sex composition

Female
38
Male
6
F : M ratio
6.33 : 1

86% female · n = 44 subjects with reported sex.

Channel counts: 69 ch (n=88 recordings)

Sampling frequencies: 250.0 Hz (n=88 recordings)

Total recording duration: 22 h 45 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 69 ch · EEG · 250 Hz · 44 subjects, 88 recordings

Live trace viewer — sub-P23 · task-JustFaces

Showing one representative recording out of 44 subjects and 88 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS007688(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS007688

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS007688(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

The Temporal Sequence of Party Leader Incongruence

Study:: ds007688 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007688, nan.

Modality: eeg. Subjects: 44; recordings: 88; tasks: 2.

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/ds007688 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007688 DOI: https://doi.org/10.18112/openneuro.ds007688.v1.0.0

Examples

>>> from eegdash.dataset import DS007688
>>> dataset = DS007688(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS007688.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds007688 to reproduce the tutorial on this dataset.

Citation

Gustavo Couto de Jesus, Bert N. Bakker, Gijs Schumacher, Joe Bathelt (2026). The Temporal Sequence of Party Leader Incongruence. 10.18112/openneuro.ds007688.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.ds007688.v1.0.0.

Related & sibling datasets

DS007864EEG · 45 subj DS002893EEG · 49 subj DS004588EEG · 42 subj DS004368EEG · 39 subj DS003751EEG · 38 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007688.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007688`
Title	The Temporal Sequence of Party Leader Incongruence
Author (year)	—
Canonical	—
Importable as	`DS007688`
Year	2026
Authors	Gustavo Couto de Jesus, Bert N. Bakker, Gijs Schumacher, Joe Bathelt
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007688.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007688: eeg dataset, 44 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#