EEGdash›OpenNeuro›DS008192

Iss. 8192 · 122 subjects · 725 recordings · CC0

Dataset Brief · InterGenSynchrony Dataset

DS008192: fnirs dataset, 122 subjects#

InterGenSynchrony Dataset

Access recordings and metadata through EEGDash.

Citation: Ryssa Moffat, Emily S. Cross (2026). InterGenSynchrony Dataset. 10.18112/openneuro.ds008192.v1.0.5

Modality: fnirs Subjects: 122 Recordings: 725 License: CC0 Source: openneuro

Metadata: Complete (100%)

122-participant fNIRS dataset — InterGenSynchrony Dataset.

Data & curation Ryssa Moffat · Emily S. Cross
Senior author Emily S. Cross
Year 2026 · Distributed via OpenNeuro
Funding Two Cortivision Photon Cap devices were provided to Ryssa Moffat through the Cortivision Pathfinder Program. Oil pastels were provided to Ryssa Moffat by Caran D’Ache. Ryssa Moffat and Emily S. Cross were supported by the Social Brain Sciences Lab at ETH Zurich.

fNIRS · 52 ch5, 6 HzBIDS 1.7.0Task · drawing6 sessions

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 DS008192

dataset = DS008192(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 = DS008192(cache_dir="./data", subject="01")

Advanced query

dataset = DS008192(
    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{ds008192,
  title = {InterGenSynchrony Dataset},
  author = {Ryssa Moffat and Emily S. Cross},
  doi = {10.18112/openneuro.ds008192.v1.0.5},
  url = {https://doi.org/10.18112/openneuro.ds008192.v1.0.5},
}

§ 02Study · The README

About This Dataset#

A longitudinal fNIRS hyperscanning dataset of same generation and intergenerational relationship

development and collaboration.

Social Brain Sciences Lab, ETH Zurich

InterGenSynchrony Dataset

Citation

Moffat, R., & Cross, E. S. (2026). InterGenSynchrony Dataset [Data set]. OpenNeuro. https://openneuro.org/datasets/ds008192/

Licence

The InterGenSynchrony Dataset is shared under a CC0 licence.

View full README

InterGenSynchrony Dataset

Citation

Moffat, R., & Cross, E. S. (2026). InterGenSynchrony Dataset [Data set]. OpenNeuro. https://openneuro.org/datasets/ds008192/

Licence

The InterGenSynchrony Dataset is shared under a CC0 licence.

Dataset Structure

To describe the content of .csv files, we provide an easy-to-read Data Dictionary, as well computer-readable .json files, which are named to match the relevant .csv files.

InterGenSynch

├── DataDictionary.html
├── dataset_description.json
├── participants.json
├── participants.tsv
├── README.html
├── README.md
├── sourcedata
|   ├── hyperscanning
|   │   ├── dyadList.csv
|   │   ├── dyadList.json
|   │   │
|   │   ├── behaviour
|   │   │   ├── codedbehaviour.json
|   │   │   ├── experimenterratings.json
|   │   │   ├── sub-all_ses-all_codedbehaviour.csv
|   │   │   └── sub-all_ses-all_experimenterratings.csv
|   │   │
|   │   ├── coordinates
|   │   │   ├── channelLengths.json
|   │   │   ├── distancesToRoi.json
|   │   │   ├── optodeCoordinates.json
|   │   │   ├── sub-101
|   │   │   │   ├── ses-01
|   │   │   │   │   ├── sub-101_ses-01_acq-dyad1001_channelLengths.csv
|   │   │   │   │   ├── sub-101_ses-01_acq-dyad1001_distancesToRoi.csv
|   │   │   │   │   └── sub-101_ses-01_acq-dyad1001_optodeCoordinates.csv
|   │   │   │   ├── ses-02
|   │   │   │   └── ...
|   │   │   ├── sub-102
|   │   │   └── ...
|   │   │
|   │   ├── drawings
|   │   │   ├── alone
|   │   │   │   ├── sub-101_ses-01_acq-dyad1001_task-drawingalone_artwork.tif
|   │   │   │   ├── sub-101_ses-02_acq-dyad1001_task-drawingalone_artwork.tif
|   │   │   │   └── ...
|   │   │   └── codrawing
|   │   │       ├── ses-01_acq-dyad1001_task-codrawing1_artwork.tif
|   │   │       ├── ses-01_acq-dyad1001_task-codrawing2_artwork.tif
|   │   │       └── ...
|   │   │
|   │   ├── mocap
|   │   │   ├── mocap_IDs.csv
|   │   │   ├── mocap.json
|   │   │   ├── mocap_IDs.json
|   │   │   ├── acq-dyad1001
|   │   │   │   ├── ses-01
|   │   │   │   │   ├── ses-01_acq-dyad1001_task-drawingalone_mocap.csv
|   │   │   │   │   ├── ses-01_acq-dyad1001_task-codrawing1_mocap.csv
|   │   │   │   │   ├── ses-01_acq-dyad1001_task-codrawing2_mocap.csv
|   │   │   │   │   └── ses-01_acq-dyad1001_task-collaborativetask_mocap.csv
|   │   │   │   ├── ses-02
|   │   │   │   └── ...
|   │   │   ├── acq-dyad1002
|   │   │   └── ...
|   │   │
|   │   ├── qualitative
|   │   │   ├── exitinterviews
|   │   │   │   ├── exitinterview_questions.json
|   │   │   │   ├── sub-101_ses-06_acq-dyad1001_exitinterview.txt
|   │   │   │   ├── sub-102_ses-06_acq-dyad1002_exitinterview.txt
|   │   │   │   └── ...
|   │   │   └── textcomments
|   │   │       ├── sub-all_ses-all_textcomments.csv
|   │   │       └── textcomments.json
|   │   │
|   │   └── selfreport
|   │       ├── pre-selfreport-raw.json
|   │       ├── post-selfreport-raw.json
|   │       ├── sub-all_ses-all_pre-selfreport-raw.csv
|   │       └── sub-all_ses-all_post-selfreport-raw.csv
|   │
|   └── ratings
|       ├── external_ratings_of_drawings.csv
|       └── external_ratings_of_drawings.json
│
├── sub-101
│   ├── ses-02
│   │   ├── nirs
│   │   │   ├── sub-101_ses-02_acq-dyad1001_coordsystem.json
│   │   │   ├── sub-101_ses-02_acq-dyad1001_optodes.tsv
│   │   │   ├── sub-101_ses-02_task-drawing_acq-dyad1001_run-01_channels.tsv
│   │   │   ├── sub-101_ses-02_task-drawing_acq-dyad1001_run-01_events.json
│   │   │   ├── sub-101_ses-02_task-drawing_acq-dyad1001_run-01_events.tsv
│   │   │   ├── sub-101_ses-02_task-drawing_acq-dyad1001_run-01_nirs.json
│   │   │   └── sub-101_ses-02_task-drawing_acq-dyad1001_run-01_nirs.snirf
│   │   └── sub-101_ses-02_scans.tsv
│   ├── ses-03
│   └── ...
├── sub-102
└── ...

Participants

Main hyperscanning experiment: Participants include 122 community-dwelling adults from Zurich, Switzerland. Of these, 31 were older adults (aged 69+ years) and 91 were younger adults (aged 18–35 years). Participants were assigned to intergenerational dyads (n = 31) and same generation dyads (n = 30) based on availability for sessions (e.g., matching people available at the same day/time for 6 consecutive weeks). External rating of drawings: External raters, recruited via Prolific, were 103 residents of the United Kingdom, who were 18 years of age or above, proficient English speakers, who had previously completed a minimum of 50 tasks on Prolific with a 100% approval rate.

Data Modalities

The dataset is divided into two parts.

The root folder contains the fNIRS recordings from the main longitudinal hyperscanning experiment. Inside the sourcedata/ folder, the hyperscanning/ folder contains the other data modalities from the main longitudinal hyperscanning experiment. The ratings/ folder contains external raters’ ratings of the drawings produced in the main experiment.

Root folder

fNIRS

Inside the root folder, the fNIRS data are organised according to BIDS conventions. This includes a folder per participant (e.g., sub-101/) that contains a folder for each session (e.g., ses-02/). The ses-XX/ folder contains a file listing recordings (*_scans.tsv) and a nirs/ folder with: - raw fNIRS recordings (*_nirs.snirf) - device and recording information (*_nirs.json) - event (trigger) information (*_events.tsv) - channel information, including channels identified as having no cardiac oscillation by visual inspection (*_channels.tsv) - optode montage information in channel-space (*_optodes.tsv) - MNI coordinate system information (*_coordsystem.json)

Sourcedata

Hyperscanning

Behaviour

behaviour/ contains - scored behaviour during collaborative tasks (sub-all_ses-all_codedbehaviour.csv) - experimenter ratings of simultaneous drawing behaviour (sub-all_ses-all_experimenterratings.csv)

Coordinates

coordinates/ contains a folder per participant with a subfolder per session (sub-XXX/ses-XX/) that contains: - digitised positions of optodes in MNI coordinates (*_optodeCoordinates.csv) - the distance between source-detector pairs (*_channelLengths.csv) - the distance from the centre of each channel to the centre of each region of interest (*_distancesToRoi.csv)

Drawings

drawings/ contains two subfolders: - alone/ — images of drawings made by participants individually - codrawing/ — images of drawings made by dyads together

Motion Capture

mocap/ contains a folder per dyad (e.g., acq-dyad1001/) with a subfolder per session (ses-XX/) that contains the 2D motion capture coordinates per drawing and collaborative activity separately.

Qualitative

qualitative/ contains two subfolders: - exitinterviews/ — transcripts of exit interviews per participant - textcomments/ — text-based reflections from all participants in a single file (sub-all_ses-all_textcomments.csv)

Self-Report

selfreport/ contains raw self-reported questionnaire responses recorded: - before each session (*_pre-selfreport-raw.csv) - after each session (*_post-selfreport-raw.csv).

Ratings

ratings/ contains external raters’ ratings of the co-drawn drawings produced in the main dataset.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=122, range 18–85 yr, mean 36.2 yr · sex per subject not reported)

152025306570758085

Sex composition

Female
73
Male
48
F : M ratio
1.52 : 1

60% female · n = 121 subjects with reported sex.

Channel counts: 52 ch (n=725 recordings)

Sampling frequencies (Hz)

4.64.74.84.84.84.84.84.84.94.94.94.94.94.94.94.94.95.05.05.05.05.05.05.05.05.055.05.05.05.05.15.15.15.15.15.15.15.15.15.15.15.15.15.25.25.25.25.25.25.25.35.35.35.35.35.35.45.45.45.55.65.6

Total recording duration: 265 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 52 ch · fNIRS · 5, 6 Hz · 122 subjects, 725 recordings

Electrode layout — fNIRS · 26 sensors — 26 channels

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.DS008192(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS008192

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

class eegdash.dataset.DS008192(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

InterGenSynchrony Dataset

Study:: ds008192 (OpenNeuro)
Author (year):: —
Canonical:: —

Also importable as: DS008192.

Modality: fnirs; Subject type: Unknown. Subjects: 122; recordings: 725; 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/ds008192 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008192 DOI: https://doi.org/10.18112/openneuro.ds008192.v1.0.5

Examples

>>> from eegdash.dataset import DS008192
>>> dataset = DS008192(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 — DS008192.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 ds008192 to reproduce the tutorial on this dataset.

Citation

Ryssa Moffat, Emily S. Cross (2026). InterGenSynchrony Dataset. 10.18112/openneuro.ds008192.v1.0.5

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds008192.v1.0.5.

Related & sibling datasets

DS004929FNIRS · 12 subj DS005930FNIRS · 12 subj DS007816FNIRS · 6 subj ON007990FNIRS · 63 subj ON006902FNIRS · 42 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels

Provenance

CC0 · 10.18112/openneuro.ds008192.v1.0.5

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS008192`
Title	InterGenSynchrony Dataset
Author (year)	—
Canonical	—
Importable as	`DS008192`
Year	2026
Authors	Ryssa Moffat, Emily S. Cross
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds008192.v1.0.5
Source links	OpenNeuro \| NeMAR \| Source URL

DS008192: fnirs dataset, 122 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#