DS006761#

Neural decoding of competitive decision-making in Rock-Paper-Scissors

Access recordings and metadata through EEGDash.

Citation: Moerel, Denise, Grootswagers, Tijl, Chin, Jessica L.L., Ciardo, Francesca, Nijhuis, Patti, Quek, Genevieve L., Smit, Sophie, Varlet, Manuel (2025). Neural decoding of competitive decision-making in Rock-Paper-Scissors. 10.18112/openneuro.ds006761.v1.0.0

Modality: eeg Subjects: 31 Recordings: 129 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006761

dataset = DS006761(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006761(cache_dir="./data", subject="01")

Advanced query

dataset = DS006761(
    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{ds006761,
  title = {Neural decoding of competitive decision-making in Rock-Paper-Scissors},
  author = {Moerel, Denise and Grootswagers, Tijl and Chin, Jessica L.L. and Ciardo, Francesca and Nijhuis, Patti and Quek, Genevieve L. and Smit, Sophie and Varlet, Manuel},
  doi = {10.18112/openneuro.ds006761.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006761.v1.0.0},
}

About This Dataset#

Experiment Details

Participants played a computerised version of the competitive Rock-Paper-Scissors game (480 games). We recorded 64 channel EEG from 62 participants, grouped into 31 pairs.

Experiment length: 1 hour

More information:

https://doi.org/10.17605/OSF.IO/YJXKN (OSF repository with more information and analysis code)

Moerel, D., Grootswagers, T., Chin, J. L., Ciardo, F., Nijhuis, P., Quek, G. L., Smit, S. & Varlet, M. (2025). Neural decoding of competitive decision-making in Rock-Paper-Scissors. Social Cognitive And Affective Neuroscience, nsaf101. doi: https://doi.org/10.1093/scan/nsaf101

Dataset Information#

Dataset ID

DS006761

Title

Neural decoding of competitive decision-making in Rock-Paper-Scissors

Year

2025

Authors

Moerel, Denise, Grootswagers, Tijl, Chin, Jessica L.L., Ciardo, Francesca, Nijhuis, Patti, Quek, Genevieve L., Smit, Sophie, Varlet, Manuel

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006761.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006761,
  title = {Neural decoding of competitive decision-making in Rock-Paper-Scissors},
  author = {Moerel, Denise and Grootswagers, Tijl and Chin, Jessica L.L. and Ciardo, Francesca and Nijhuis, Patti and Quek, Genevieve L. and Smit, Sophie and Varlet, Manuel},
  doi = {10.18112/openneuro.ds006761.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006761.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: 129

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 2048.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Decision-making

Files & format

  • Size on disk: 78.0 GB

  • File count: 129

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006761.v1.0.0

Provenance

API Reference#

Use the DS006761 class to access this dataset programmatically.

class eegdash.dataset.DS006761(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006761. Modality: eeg; Experiment type: Decision-making; 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/ds006761 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006761

Examples

>>> from eegdash.dataset import DS006761
>>> dataset = DS006761(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#