DS006629#

SINGSING

Access recordings and metadata through EEGDash.

Citation: Valerie Chanoine, Jean-Michel Badier, Mireille Besson, Talya Inbar (2025). SINGSING. 10.18112/openneuro.ds006629.v1.0.1

Modality: meg Subjects: 19 Recordings: 196 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006629

dataset = DS006629(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006629(cache_dir="./data", subject="01")

Advanced query

dataset = DS006629(
    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{ds006629,
  title = {SINGSING},
  author = {Valerie Chanoine and Jean-Michel Badier and Mireille Besson and Talya Inbar},
  doi = {10.18112/openneuro.ds006629.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006629.v1.0.1},
}

About This Dataset#

We presented twenty adult participants with harmonic complex sound (HCS) stimuli that varied in frequency in an auditory oddball protocol during simultaneous EEG and MEG recording (for details, see Inbar et al., 2025)

References

Inbar, T.C., Badier, JM., Bénar, C. et al. Pre-attentive Pitch Processing of Harmonic Complex Sounds at Sensor and Source Levels: Comparing Simultaneously Recorded EEG and MEG Data. Brain Topogr 38, 71 (2025). https://doi.org/10.1007/s10548-025-01147-6

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896file:///media/chanoine/My%20Passport/SINGSING/data/COMB/preproc/FIF/BIDS/dataset_description.json

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110.https://doi.org/10.1038/sdata.2018.110

Dataset Information#

Dataset ID

DS006629

Title

SINGSING

Year

2025

Authors

Valerie Chanoine, Jean-Michel Badier, Mireille Besson, Talya Inbar

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006629.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006629,
  title = {SINGSING},
  author = {Valerie Chanoine and Jean-Michel Badier and Mireille Besson and Talya Inbar},
  doi = {10.18112/openneuro.ds006629.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006629.v1.0.1},
}

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: 19

  • Recordings: 196

  • Tasks: 2

Channels & sampling rate

  • Channels: 64 (38), 339 (38)

  • Sampling rate (Hz): 250.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Perception

Files & format

  • Size on disk: 11.2 GB

  • File count: 196

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006629.v1.0.1

Provenance

API Reference#

Use the DS006629 class to access this dataset programmatically.

class eegdash.dataset.DS006629(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006629. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 19; recordings: 38; tasks: 2.

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/ds006629 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006629

Examples

>>> from eegdash.dataset import DS006629
>>> dataset = DS006629(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#