EEGdash›OpenNeuro›DS006861

Iss. 6861 · 120 subjects · 239 recordings · CC0

Dataset Brief · Targeted Neuromodulation of the Left Dorsolateral Prefrontal…

DS006861: eeg dataset, 120 subjects#

Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals

Citation: Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek (20). Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals. 10.18112/openneuro.ds006861.v1.0.2

120-participant EEG dataset — Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation 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 · 37 ch1000 HzBIDS 1.8.0Task · EmotionProcessingandRegulationta2 sessionsHealthyVisualAffect

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 DS006861

dataset = DS006861(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 = DS006861(cache_dir="./data", subject="01")

Advanced query

dataset = DS006861(
    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{ds006861,
  title = {Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals},
  author = {Szymon Mąka and Marta Chrustowicz and Łukasz Okruszek},
  doi = {10.18112/openneuro.ds006861.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds006861.v1.0.2},
}

§ 02Study · The README

About This Dataset#

This repository provides EEG recordings and behavioral data from the **Emotion Processing and Regulation**task conducted with **transcranial direct current stimulation (tDCS)**.

*\*Preregistration:* https://osf.io/qdp3w

Each participant took part in two experimental sessions:

* **``ses-1``** — Sham stimulation * **``ses-2``** — Active stimulation

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

The order of sham/active conditions was counterbalanced across participants.

Participants

* **N = 120** right‑handed, neurologically healthy adults with normal or corrected‑to‑normal vision.

*\*Missing data:* Participant sub-005 completed only ses-1 due to a recording error during ses-2.

View full README

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

The order of sham/active conditions was counterbalanced across participants.

Participants

* **N = 120** right‑handed, neurologically healthy adults with normal or corrected‑to‑normal vision.

*\*Missing data:* Participant sub-005 completed only ses-1 due to a recording error during ses-2.

Experimental Task

Participants completed 120 trials per 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 watched or reappraised it as instructed. After each image, participants rated its arousal and then valence on separate 9-point scales.

tDCS Stimulation

System: Starstim 8 (Neuroelectrics, Spain) with NIC2 software.

Electrode Montage

Stimulation was targeted to the dorsolateral prefrontal cortex (dlPFC) in two alternative montages: * **Right dlPFC stimulation**

*\*Anode:* **F4** *\*Returns:* **FP2, FZ, FC2, FC6**

* **Left dlPFC stimulation**
*\*Anode:* **F3** *\*Returns:* **FP1, FZ, FC1, FC5**

Electrode areas *\*Anodal:* 8 cm² *\*Return:* π cm² *\*Ground:* left earlobe

Stimulation Protocol

*\*Active:* 2 mA for 20 min (with 30 s ramp‑up) *\*Sham:* only ramp‑up periods at start and end; no sustained current * *Questionnaires:* After each session, participants completed the **tDCS Sensation Questionnaire**(Polish version: https://osf.io/ufszr) to evaluate potential side effects. Additionally, after the final session, they indicated whether they believed each session involved*real*,*sham*, or*I don’t know* stimulation to assess **blinding effectiveness**.

EEG Acquisition

*\*Cap:* 64‑channel QuickCap (32 EEG electrodes used) *\*Amplifier:* Neuroscan SynampsRT *\*Sampling rate:* 1000 Hz *\*Impedance:* kept < 10 kΩ Active EEG electrodes (32): FP1, FP2, F7, F3, FZ, F4, F8, FT7, FC3, FCZ, FC4, FT8, T7, C3, CZ, C4, T8, M1, TP7, CP3, CPZ, CP4, TP8, M2, P7, P3, PZ, P4, P8, O1, OZ, O2 Additional sensors *\*EOG:* Horizontal (HEO) and Vertical (VEO) channels were available on the cap but **were not connected** during recording.

* Physio: ECG and GSR/EDA were recorded via auxiliary channels.

EEG Preprocessing

All preprocessing was performed in **MATLAB R2020b**using ** EEGLAB 2023.0**and **ERPLAB 9.10**. The full, commented pipeline is provided in code/Preprocessing_EEG.m.

Steps

Band‑pass filter: 0.1–30 Hz (zero‑phase Hamming‑windowed FIR)

Downsample**to **250 Hz

Re‑reference to average mastoids (M1, M2)

**Bad‑channel detection**using*clean_rawdata* (autocorrelation criterion = 0.8)

**ICA**with*runica*

Automatic IC rejection**using ** 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: Step 1 – absolute amplitude on channels 1–30, epochs rejected if amplitude exceeded ±200 µV within −200 to 5000 ms. Step 2 – FASTER epoch_properties on channels 1–30, epochs rejected if any metric exceeded |z| > 2.

11. Condition‑wise averaging using ERPLAB

Derivatives & Ancillary Data

derivatives/processed_erps/

Averaged ERP files (.erp) for each participant and session after preprocessing.

derivatives/sideeffectsblinding_effectiveness/

* side_effects_blinding_effectiveness_english.csv — _blinding effectiveness and side effects questionnaire * side_effects_blinding_effectiveness_data_dictionary.csv — data dictionary with variable names and value coding

code/

* MATLAB preprocessing script and documentation: Preprocessing_EEG.m

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=120, range 18–35 yr, mean 24.1 yr · sex per subject not reported)

1520253035

Sex composition

Female
70
Male
50
F : M ratio
1.40 : 1

58% female · n = 120 subjects with reported sex.

Channel counts: 37 ch (n=239 recordings)

Sampling frequencies: 1000.0 Hz (n=239 recordings)

Total recording duration: 99 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 37 ch · EEG · 1000 Hz · 120 subjects, 239 recordings

Live trace viewer — sub-021 · ses-2 · task-EmotionProcessingandRegulationtask

Showing one representative recording out of 120 subjects and 239 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.DS006861(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Maka2025_Targeted

Canonical—

Importable asDS006861 · Maka2025_Targeted

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

class eegdash.dataset.DS006861(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals

Study:: ds006861 (OpenNeuro)
Author (year):: Maka2025_Targeted
Canonical:: —

Also importable as: DS006861, Maka2025_Targeted.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 120; recordings: 239; 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/ds006861 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006861 DOI: https://doi.org/10.18112/openneuro.ds006861.v1.0.2

Examples

>>> from eegdash.dataset import DS006861
>>> dataset = DS006861(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/ds006861 · pull with datasets.load_dataset("EEGDash/ds006861").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006861.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 ds006861 to reproduce the tutorial on this dataset.

Citation

Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek (20). Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals. 10.18112/openneuro.ds006861.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds006861.v1.0.2.

Related & sibling datasets

DS006866EEG · 148 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.ds006861.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006861`
Title	Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals
Author (year)	`Maka2025_Targeted`
Canonical	—
Importable as	`DS006861`, `Maka2025_Targeted`
Year	20
Authors	Szymon Mąka, Marta Chrustowicz, Łukasz Okruszek
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006861.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS006861: eeg dataset, 120 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#