EEGdash›OpenNeuro›DS006866

Iss. 6866 · 148 subjects · 148 recordings · CC0

Dataset Brief · Discrepancy between self-report and neurophysiological marker…

DS006866: eeg dataset, 148 subjects#

Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals

Citation: Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek (20). Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals. 10.18112/openneuro.ds006866.v1.0.0

148-participant EEG dataset — Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals.

Data & curation Szymon Mąka · Marta Chrustowicz · Łukasz Okruszek
Year 20 · Distributed via OpenNeuro
Funding National Science Centre, Poland (Grant No: 2019/35/B/HS6/00517)

EEG · 69 ch1000 HzBIDS 1.8.0Task · EmotionProcessingandRegulationtaHealthyVisualAffect

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 DS006866

dataset = DS006866(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 = DS006866(cache_dir="./data", subject="01")

Advanced query

dataset = DS006866(
    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{ds006866,
  title = {Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals},
  author = {Szymon Mąka and Marta Chrustowicz and Łukasz Okruszek},
  doi = {10.18112/openneuro.ds006866.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006866.v1.0.0},
}

§ 02Study · The README

About This Dataset#

This dataset contains EEG recordings and behavioral data from the Emotion Processing and Regulation task with static emotional stimuli.

*\*Preregistration:* https://osf.io/g8qey *\*Preprint:* https://osf.io/preprints/psyarxiv/v9dt3_v2

* **N = 148** right-handed, neurologically healthy adults with normal or corrected-to-normal vision.

Emotion Processing and Regulation Task (Static Stimuli) — EEG Dataset

Experimental Design

The single session comprised 240 trials, split evenly across six conditions defined by stimulus content type Participants completed 240 trials in a single session, evenly allocated to a 2 (content: social, non-social) × 3 (regulation requirement: watch-neutral, watch-negative, reappraise-negative) factorial design. On each trial, they viewed a static image for 5 s and either passively watched it or reappraised it as instructed. They then rated arousal and subsequently valence of the image on separate 9-point scales.

EEG Data Acquisition

*\*EEG Cap:* 64-channel QuickCap *\*Amplifier:* Neuroscan SynampsRT

View full README

Emotion Processing and Regulation Task (Static Stimuli) — EEG Dataset

Experimental Design

The single session comprised 240 trials, split evenly across six conditions defined by stimulus content type Participants completed 240 trials in a single session, evenly allocated to a 2 (content: social, non-social) × 3 (regulation requirement: watch-neutral, watch-negative, reappraise-negative) factorial design. On each trial, they viewed a static image for 5 s and either passively watched it or reappraised it as instructed. They then rated arousal and subsequently valence of the image on separate 9-point scales.

EEG Data Acquisition

*\*EEG Cap:* 64-channel QuickCap *\*Amplifier:* Neuroscan SynampsRT *\*Sampling rate:* 1000 Hz *\*Impedance:* below 5 kΩ

Recorded Channels

FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, M1, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, M2, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, CB1, O1, OZ, O2, CB2

Additional Sensors

* HEO (Horizontal EOG) * VEO (Vertical EOG) * EKG

* GSR/EDA

EEG Preprocessing

All preprocessing was conducted in **MATLAB R2020b**using ** EEGLAB 2023.0**and **ERPLAB 9.10**. The preprocessing pipeline and fully commented scripts are available in code/Preprocessing_EEG.m.

Summary of preprocessing steps

Band-pass filtering: 0.1–30 Hz (zero-phase Hamming-windowed FIR)

Downsampling: 250 Hz

Re-referencing: average mastoids (M1, M2)

Automatic bad-channel rejection: clean_rawdata (autocorrelation = 0.8)

ICA: runica algorithm

Automatic component rejection: ADJUST**and ** SASICA

Spherical interpolation of removed channels

Epoching: −200 to 5000 ms relative to stimulus onset

Baseline correction: −200 ms pre-stimulus

Artifact rejection: ±100 µV within 200 ms moving window (100 ms step)

11. Condition averaging: using ERPLAB

Derivatives & Supplemental Data

derivatives/processed_erps/

Contains averaged ERP files (.erp) for each participant after preprocessing.

code/

Includes MATLAB preprocessing scripts and documentation (Preprocessing_EEG.m).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=148, range 18–35 yr, mean 25.3 yr · sex per subject not reported)

1520253035

Sex composition

Other
148

Channel counts: 69 ch (n=148 recordings)

Sampling frequencies: 1000.0 Hz (n=148 recordings)

Total recording duration: 123 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 69 ch · EEG · 1000 Hz · 148 subjects, 148 recordings

Live trace viewer — sub-021 · task-EmotionProcessingandRegulationtask

Showing one representative recording out of 148 subjects and 148 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.DS006866(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Maka2025_Discrepancy

Canonical—

Importable asDS006866 · Maka2025_Discrepancy

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

class eegdash.dataset.DS006866(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals

Study:: ds006866 (OpenNeuro)
Author (year):: Maka2025_Discrepancy
Canonical:: —

Also importable as: DS006866, Maka2025_Discrepancy.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 148; recordings: 148; 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/ds006866 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006866 DOI: https://doi.org/10.18112/openneuro.ds006866.v1.0.0

Examples

>>> from eegdash.dataset import DS006866
>>> dataset = DS006866(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 FacePre-bundled mirror at EEGDash/ds006866 · pull with datasets.load_dataset("EEGDash/ds006866").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006866.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 ds006866 to reproduce the tutorial on this dataset.

Citation

Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek (20). Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals. 10.18112/openneuro.ds006866.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.ds006866.v1.0.0.

Related & sibling datasets

DS006861EEG · 120 subjSame authors DS005410EEG · 81 subj DS006850EEG · 63 subj DS005540EEG · 59 subj DS006648EEG · 47 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds006866.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006866`
Title	Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals
Author (year)	`Maka2025_Discrepancy`
Canonical	—
Importable as	`DS006866`, `Maka2025_Discrepancy`
Year	20
Authors	Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006866.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS006866: eeg dataset, 148 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#