DS006802#

Collaborative rule learning promotes interbrain information alignment

Access recordings and metadata through EEGDash.

Citation: Moerel, Denise, Grootswagers, Tijl, Quek, Genevieve L., Smit, Sophie, Varlet, Manuel (2025). Collaborative rule learning promotes interbrain information alignment. 10.18112/openneuro.ds006802.v1.0.0

Modality: eeg Subjects: 24 Recordings: 55 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006802

dataset = DS006802(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006802(cache_dir="./data", subject="01")

Advanced query

dataset = DS006802(
    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{ds006802,
  title = {Collaborative rule learning promotes interbrain information alignment},
  author = {Moerel, Denise and Grootswagers, Tijl and Quek, Genevieve L. and Smit, Sophie and Varlet, Manuel},
  doi = {10.18112/openneuro.ds006802.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006802.v1.0.0},
}

About This Dataset#

Experiment Details

We recorded EEG from 24 pairs of participants while they performed a 4-way categorisation task based on rules they first agreed upon together. In addition, participants did a pre- and post-test on the same stimuli.

Experiment length: 1 hour

More information:

Pre-print: Moerel, D., Grootswagers, T., Quek, G.L., Smit, S., & Varlet, M. (2025). Information alignment between interacting brains. bioRxiv. doi: https://doi.org/10.1101/2025.01.07.631802

Code: https://doi.org/10.17605/OSF.IO/HE4TU

Dataset Information#

Dataset ID

DS006802

Title

Collaborative rule learning promotes interbrain information alignment

Year

2025

Authors

Moerel, Denise, Grootswagers, Tijl, Quek, Genevieve L., Smit, Sophie, Varlet, Manuel

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006802.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006802,
  title = {Collaborative rule learning promotes interbrain information alignment},
  author = {Moerel, Denise and Grootswagers, Tijl and Quek, Genevieve L. and Smit, Sophie and Varlet, Manuel},
  doi = {10.18112/openneuro.ds006802.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006802.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: 24

  • Recordings: 55

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 2048.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: —

  • Type: Learning

Files & format

  • Size on disk: 62.2 GB

  • File count: 55

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006802.v1.0.0

Provenance

API Reference#

Use the DS006802 class to access this dataset programmatically.

class eegdash.dataset.DS006802(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006802. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 24; recordings: 24; 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/ds006802 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006802

Examples

>>> from eegdash.dataset import DS006802
>>> dataset = DS006802(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#