DS003104#

MNE-somato-data-bids dataset (anonymized)

Access recordings and metadata through EEGDash.

Citation: Lauri Parkkonen, Stefan Appelhoff, Alexandre Gramfort, Mainak Jas, Richard Höchenberger (2020). MNE-somato-data-bids dataset (anonymized). 10.18112/openneuro.ds003104.v1.0.0

Modality: meg Subjects: 1 Recordings: 12 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003104

dataset = DS003104(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003104(cache_dir="./data", subject="01")

Advanced query

dataset = DS003104(
    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{ds003104,
  title = {MNE-somato-data-bids dataset (anonymized)},
  author = {Lauri Parkkonen and Stefan Appelhoff and Alexandre Gramfort and Mainak Jas and Richard Höchenberger},
  doi = {10.18112/openneuro.ds003104.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003104.v1.0.0},
}

About This Dataset#

MNE-somato-data-bids

This dataset contains the MNE-somato-data in BIDS format.

The conversion can be reproduced through the Python script stored in the /code directory of this dataset. See the README in that directory.

The /derivatives directory contains the outputs of running the FreeSurfer

View full README

MNE-somato-data-bids

This dataset contains the MNE-somato-data in BIDS format.

The conversion can be reproduced through the Python script stored in the /code directory of this dataset. See the README in that directory.

The /derivatives directory contains the outputs of running the FreeSurfer pipeline recon-all on the MRI data with no additional commandline options (only defaults were used):

$ recon-all -i sub-01_T1w.nii.gz -s 01 -all

After the recon-all call, there were further FreeSurfer calls from the MNE API:

$ mne make_scalp_surfaces -s 01 –force $ mne watershed_bem -s 01

The derivatives also contain the forward model *-fwd.fif, which was produced using the source space definition, a *-trans.fif file, and the boundary element model (=conductor model) that lives in freesurfer/subjects/01/bem/*-bem-sol.fif.

The *-trans.fif file is not saved, but can be recovered from the anatomical landmarks in the sub-01/anat/T1w.json file and MNE-BIDS’ function get_head_mri_transform.

See: mne-tools/mne-bids for more information.

Notes on FreeSurfer

the FreeSurfer pipeline recon-all was run new for the sake of converting the somato data to BIDS format. This needed to be done to change the “somato” subject name to the BIDS subject label “01”. Note, that this is NOT “sub-01”, because in BIDS, the “sub-” is just a prefix, whereas the “01” is the subject label.

Dataset Information#

Dataset ID

DS003104

Title

MNE-somato-data-bids dataset (anonymized)

Year

2020

Authors

Lauri Parkkonen, Stefan Appelhoff, Alexandre Gramfort, Mainak Jas, Richard Höchenberger

License

CC0

Citation / DOI

10.18112/openneuro.ds003104.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003104,
  title = {MNE-somato-data-bids dataset (anonymized)},
  author = {Lauri Parkkonen and Stefan Appelhoff and Alexandre Gramfort and Mainak Jas and Richard Höchenberger},
  doi = {10.18112/openneuro.ds003104.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003104.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: 1

  • Recordings: 12

  • Tasks: 1

Channels & sampling rate

  • Channels: 306, 316

  • Sampling rate (Hz): 300.3074951171875

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: Tactile

  • Type: Perception

Files & format

  • Size on disk: 333.7 MB

  • File count: 12

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds003104.v1.0.0

Provenance

API Reference#

Use the DS003104 class to access this dataset programmatically.

class eegdash.dataset.DS003104(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003104. Modality: meg; Experiment type: Perception; Subject type: Unknown. Subjects: 1; recordings: 1; 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/ds003104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003104

Examples

>>> from eegdash.dataset import DS003104
>>> dataset = DS003104(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#