DS007471: eeg dataset, 31 subjects#

Joint agency EEG dataset

Access recordings and metadata through EEGDash.

Citation: Zijun Zhou, Anna Zamm, Justin Christensen, Vinesh Rao, Janeen Loehr (2026). Joint agency EEG dataset. 10.18112/openneuro.ds007471.v1.0.0

Modality: eeg Subjects: 31 Recordings: 31 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

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

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

Behavioural Data

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

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

Behavioural Data

Located in:sub-##/beh/ File:sub-##_task-jointaction_beh.tsv

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

  1. Twinkle Twinkle Little Star

  2. Hush Little Baby

  3. B.I.N.G.O.

  4. Yankee Doodle

  5. Constant pitch sequence with A4 as higher-pitch part

  6. Constant pitch sequence with C5 as higher-pitch part

  7. 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.

Dataset Information#

Dataset ID

DS007471

Title

Joint agency EEG dataset

Author (year)

Zhou2026

Canonical

Zhou2024

Importable as

DS007471, Zhou2026, Zhou2024

Year

2026

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
Copy-paste 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},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 31

  • Recordings: 31

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 18.78229444444444

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Other

Files & format

  • Size on disk: 8.1 GB

  • File count: 31

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007471.v1.0.0

Provenance

API Reference#

Use the DS007471 class to access this dataset programmatically.

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

Bases: EEGDashDataset

Joint agency EEG dataset

Study:

ds007471 (OpenNeuro)

Author (year):

Zhou2026

Canonical:

Zhou2024

Also importable as: DS007471, Zhou2026, Zhou2024.

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#