DS004229#

amnoise

Access recordings and metadata through EEGDash.

Citation: Maria Mittag, Eric Larson, Maggie Clarke, Samu Taulu, Patricia K. Kuhl (2022). amnoise. 10.18112/openneuro.ds004229.v1.0.3

Modality: meg Subjects: 1 Recordings: 18 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004229

dataset = DS004229(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004229(cache_dir="./data", subject="01")

Advanced query

dataset = DS004229(
    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{ds004229,
  title = {amnoise},
  author = {Maria Mittag and Eric Larson and Maggie Clarke and Samu Taulu and Patricia K. Kuhl},
  doi = {10.18112/openneuro.ds004229.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds004229.v1.0.3},
}

About This Dataset#

ILABS amnoise MEG BIDS dataset

This dataset contains MEG data from a single infant subject. For more information, see the following publications, which should be cited if you use this data:

  • Mittag, M., Larson, E., Clarke, M., Taulu, S., & Kuhl, P. K. (2021). Auditory deficits in infants at risk for dyslexia during a linguistic sensitive period predict future language. NeuroImage: Clinical, 30, 102578. https://doi.org/10.1016/j.nicl.2021.102578

  • Mittag, M., Larson, E., Taulu, S., Clarke, M., & Kuhl, P. K. (2022). Reduced Theta Sampling in Infants at Risk for Dyslexia across the Sensitive Period of Native Phoneme Learning. International Journal of Environmental Research and Public Health, 19(3), 1180. https://doi.org/10.3390/ijerph19031180

The data were converted with MNE-BIDS:

  • 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.01896

  • 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

DS004229

Title

amnoise

Year

2022

Authors

Maria Mittag, Eric Larson, Maggie Clarke, Samu Taulu, Patricia K. Kuhl

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004229.v1.0.3

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004229,
  title = {amnoise},
  author = {Maria Mittag and Eric Larson and Maggie Clarke and Samu Taulu and Patricia K. Kuhl},
  doi = {10.18112/openneuro.ds004229.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds004229.v1.0.3},
}

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: 1

  • Recordings: 18

  • Tasks: 2

Channels & sampling rate

  • Channels: 306 (2), 332 (2)

  • Sampling rate (Hz): 1200.0

  • Duration (hours): 0.0

Tags

  • Pathology: Dyslexia

  • Modality: Auditory

  • Type: Perception

Files & format

  • Size on disk: 1.8 GB

  • File count: 18

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004229.v1.0.3

Provenance

API Reference#

Use the DS004229 class to access this dataset programmatically.

class eegdash.dataset.DS004229(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004229. Modality: meg; Experiment type: Perception; Subject type: Dyslexia. Subjects: 2; recordings: 3; 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/ds004229 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004229

Examples

>>> from eegdash.dataset import DS004229
>>> dataset = DS004229(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#