EEGdash›OpenNeuro›DS007471

Iss. 7471 · 31 subjects · 31 recordings · CC0

Dataset Brief · Joint agency EEG dataset

DS007471: eeg dataset, 31 subjects#

Joint agency EEG dataset

Citation: Zijun Zhou, Anna Zamm, Justin Christensen, Vinesh Rao, Janeen Loehr (—). Joint agency EEG dataset. 10.18112/openneuro.ds007471.v1.0.0

31-participant EEG dataset — Joint agency EEG dataset.

Data & curation Zijun Zhou · Anna Zamm · Justin Christensen · Vinesh Rao · Janeen Loehr

EEG · 64 ch1000 HzBIDS 1.8.0Task · jointactionHealthyAuditoryOther

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 DS007471

dataset = DS007471(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 = DS007471(cache_dir="./data", subject="01")

Advanced query

dataset = DS007471(
    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{ds007471,
  title = {Joint agency EEG dataset},
  author = {Zijun Zhou and Anna Zamm and Justin Christensen and Vinesh Rao and Janeen Loehr},
  doi = {10.18112/openneuro.ds007471.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007471.v1.0.0},
}

§ 02Study · The README

About This Dataset#

The primary folder includes a separate folder for each pair:sub-##

Each pair folder contains:

Located in:sub-##/beh/

File:sub-##_task-jointaction_beh.tsv

Behavioural and EEG data from an EEG hyperscanning study examining cognitive and neural signals underlying the sense of joint agency during a musical joint action task

Dataset Structure

EEG Data

Located in:sub-##/eeg/ Files (BrainVision format): sub-##_task-jointaction_eeg.eeg

View full README

Behavioural and EEG data from an EEG hyperscanning study examining cognitive and neural signals underlying the sense of joint agency during a musical joint action task

Dataset Structure

EEG Data

Located in:sub-##/eeg/ Files (BrainVision format): sub-##_task-jointaction_eeg.eeg sub-##_task-jointaction_eeg.vhdr

sub-##task-jointactioneeg.vmrk

Derivatives Folder

The derivatives/ folder contains: - behavioural_all.tsv

Compiled behavioural data across all pairs.

32chanElectrodePositions.elp

Electrode positions used for EEG data acquisition and analysis.

Behavioural Data Description

The following column descriptions apply to both: - behavioural_all.tsv

- sub-##task-jointactionbeh.tsv

Pair Number

Values: 1–32

Participant Number

The first one or two digits represent the pair number.

The last digit represents seating position: - 1 = left participant - 2 = right participant

Examples: - 11 = left participant in pair 1

- 202 = right participant in pair 20

Block Number

Test block number for a given trial (1–8).

Trial Number

Each pair performed: - 8 tone sequences

4 musical duets

4 constant pitch sequences

5 joint trials per sequence

Total: - 40 test trials per pair

- Trial numbers range from 1–40

Experimental Condition

0 = constant pitch sequences

- 1 = musical duets

Part Performed

Indicates which part of the tone sequence the participant performed: - 0 = higher-pitch part (for constant pitch sequences) or melody part (for musical duets) - 1 = lower-pitch part (for constant pitch sequences) or accompaniment part (for musical duets)

Tone Sequence

Twinkle Twinkle Little Star

Hush Little Baby

B.I.N.G.O.

Yankee Doodle

Constant pitch sequence with A4 as higher-pitch part

Constant pitch sequence with C5 as higher-pitch part

Constant pitch sequence with E♭5 as higher-pitch part

8. Constant pitch sequence with F♯5 as higher-pitch part

Joint Agency Ratings

Self-reported rating scale: 1–7

Mean Synchronization Performance

The mean synchronization performance for each trial was calculated as follows. First, we calculated the absolute asynchrony between the two participants’ note onsets at each beat. Then, we converted each asynchrony to a proportion of the inter-onset interval (IOI) from the preceding note onset to the current note onset, which we averaged across the two participants and across all beats in the sequence.

Standard Deviation (SD) of Synchronization Performance

The SD of synchronization performance was defined as the standard deviation of the asynchronies across all beats in a given each trial.

EEG Data Description

For each EEG dataset within each pair’s folder: - Channels 1–32: left participant EEG - Channels 33–64: right participant EEG

Data are stored in BrainVision format.

Event Codes (Test Section)

The following event markers are present during the test section (see Figure 1 for schematic reference): - S1 – the beginning of the test trials portion of the experiment - S10 – a condition marker indicating the beginning of a block of musical duets - S11 – a condition marker indicating the beginning of a block of constant pitch sequences - S105 – the start of each trial, triggered by pressing the space bar - S128 – The first five S128s mark the metronome tone onsets. Remaining S128s mark the tone onsets from the left participant’s e-music box. - S4 – tone onsets from the right participant’s e-music box - S2 – the end of the left participant’s performance, marked one beat after the last of their 16-beat tone sequence - S3 – the end of the right participant’s performance, marked one beat after the last of their 16-beat tone sequence - S106 – the end of each trial after the rating scales were completed

- S107 – the end of each block

Figure

Illustration of the event codes occurring over time in the dataset.

Notes

Data are organized in BIDS format.

BrainVision files (.eeg, .vhdr, .vmrk) contain raw hyperscanning EEG data.

Behavioural data are provided per pair and as a compiled dataset in the derivatives folder.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 64 ch (n=30 recordings)

Sampling frequencies: 1000.0 Hz (n=31 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 1000 Hz · 31 subjects, 31 recordings

Live trace viewer — sub-13 · task-jointaction

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

§ 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.DS007471(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Zhou2026

Canonical—

Importable asDS007471 · Zhou2026

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

class eegdash.dataset.DS007471(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Joint agency EEG dataset

Study:: ds007471 (OpenNeuro)
Author (year):: Zhou2026
Canonical:: —

Also importable as: DS007471, Zhou2026.

Modality: eeg; Experiment type: Other; Subject type: Healthy. 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

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/ds007471 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007471 DOI: https://doi.org/10.18112/openneuro.ds007471.v1.0.0

Examples

>>> from eegdash.dataset import DS007471
>>> dataset = DS007471(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 — DS007471.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 ds007471 to reproduce the tutorial on this dataset.

Citation

Zijun Zhou, Anna Zamm, Justin Christensen, Vinesh Rao, Janeen Loehr (n.d.). Joint agency EEG dataset. 10.18112/openneuro.ds007471.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.ds007471.v1.0.0.

Related & sibling datasets

DS004572EEG · 52 subj DS005876EEG · 29 subj DS007322EEG · 57 subj DS005795EEG · 34 subj DS005345EEG · 26 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007471.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007471`
Title	Joint agency EEG dataset
Author (year)	`Zhou2026`
Canonical	—
Importable as	`DS007471`, `Zhou2026`
Year	—
Authors	Zijun Zhou, Anna Zamm, Justin Christensen, Vinesh Rao, Janeen Loehr
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007471.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007471: eeg dataset, 31 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#