DS000248#

MNE-Sample-Data

Access recordings and metadata through EEGDash.

Citation: Alexandre Gramfort, Matti S Hämäläinen (2018). MNE-Sample-Data. 10.18112/openneuro.ds000248.v1.2.4

Modality: meg Subjects: 1 Recordings: 22 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS000248

dataset = DS000248(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS000248(cache_dir="./data", subject="01")

Advanced query

dataset = DS000248(
    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{ds000248,
  title = {MNE-Sample-Data},
  author = {Alexandre Gramfort and Matti S Hämäläinen},
  doi = {10.18112/openneuro.ds000248.v1.2.4},
  url = {https://doi.org/10.18112/openneuro.ds000248.v1.2.4},
}

About This Dataset#

MNE-Sample-Data

The MNE software is accompanied by a sample data set. These data were acquired with the Neuromag Vectorview system at MGH/HMS/MIT Athinoula A. Martinos Center Biomedical Imaging. EEG data from a 60-channel electrode cap was acquired simultaneously with the MEG. The original MRI data set was acquired with a Siemens 1.5 T Sonata scanner using an MPRAGE sequence.

In the MEG/EEG experiment, checkerboard patterns were presented into the left and right visual field, interspersed by tones to the left or right ear. The interval between the stimuli was 750 ms. Occasionally a smiley face was presented at the center of the visual field. The subject was asked to press a key with the right index finger as soon as possible after the appearance of the face.

View full README

MNE-Sample-Data

The MNE software is accompanied by a sample data set. These data were acquired with the Neuromag Vectorview system at MGH/HMS/MIT Athinoula A. Martinos Center Biomedical Imaging. EEG data from a 60-channel electrode cap was acquired simultaneously with the MEG. The original MRI data set was acquired with a Siemens 1.5 T Sonata scanner using an MPRAGE sequence.

In the MEG/EEG experiment, checkerboard patterns were presented into the left and right visual field, interspersed by tones to the left or right ear. The interval between the stimuli was 750 ms. Occasionally a smiley face was presented at the center of the visual field. The subject was asked to press a key with the right index finger as soon as possible after the appearance of the face.

Freesurfer derivatives

  • Calls from the command line: - recon-all -i sub-01/anat/sub-01_T1w.nii.gz -s sub-01 -all - mne make_scalp_surfaces -s sub-01 --overwrite --force - mne flash_bem -s sub-01 --overwrite - mne watershed_bem -s sub-01 --overwrite

References

  1. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. Hämäläinen, MNE software for processing MEG and EEG data, NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-8119

  1. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, R. Goj, M. Jas, T. Brooks, L. Parkkonen, M. Hämäläinen, MEG and EEG data analysis with MNE-Python, Frontiers in Neuroscience, Volume 7, 2013, ISSN 1662-453X”

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. http://doi.org/10.1038/sdata.2018.110

References

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

DS000248

Title

MNE-Sample-Data

Year

2018

Authors

Alexandre Gramfort, Matti S Hämäläinen

License

CC0

Citation / DOI

10.18112/openneuro.ds000248.v1.2.4

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds000248,
  title = {MNE-Sample-Data},
  author = {Alexandre Gramfort and Matti S Hämäläinen},
  doi = {10.18112/openneuro.ds000248.v1.2.4},
  url = {https://doi.org/10.18112/openneuro.ds000248.v1.2.4},
}

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: 22

  • Tasks: 2

Channels & sampling rate

  • Channels: 60, 376, 306, 315

  • Sampling rate (Hz): 600.614990234375

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Multisensory

  • Type: Attention

Files & format

  • Size on disk: 177.6 MB

  • File count: 22

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds000248.v1.2.4

Provenance

API Reference#

Use the DS000248 class to access this dataset programmatically.

class eegdash.dataset.DS000248(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds000248. Modality: meg; Experiment type: Attention; Subject type: Healthy. 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/ds000248 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000248

Examples

>>> from eegdash.dataset import DS000248
>>> dataset = DS000248(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#