DS005241#

NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis

Access recordings and metadata through EEGDash.

Citation: Amilleah Rodriguez, Dan Zhao, Kyra Wilson, Ritika Saboo, Sergey V Samsonau, Alec Marantz (2024). NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis. 10.18112/openneuro.ds005241.v1.1.0

Modality: meg Subjects: 24 Recordings: 554 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005241

dataset = DS005241(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS005241(cache_dir="./data", subject="01")

Advanced query

dataset = DS005241(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds005241,
  title = {NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis},
  author = {Amilleah Rodriguez and Dan Zhao and Kyra Wilson and Ritika Saboo and Sergey V Samsonau and Alec Marantz},
  doi = {10.18112/openneuro.ds005241.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds005241.v1.1.0},
}

About This Dataset#

KIT/Yokogawa MEG system with 208 magnetometer channels

24 subjects amounting to over 17 hours of data

Supplementary code can be found here_

References

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	`DS005241`
Title	NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis
Year	2024
Authors	Amilleah Rodriguez, Dan Zhao, Kyra Wilson, Ritika Saboo, Sergey V Samsonau, Alec Marantz
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005241.v1.1.0
Source links	OpenNeuro \| NeMAR \| Source URL

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: 24
Recordings: 554
Tasks: 2

Channels & sampling rate

Channels: 256 (207), 207 (27)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS005241 class to access this dataset programmatically.

class eegdash.dataset.DS005241(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005241. Modality: meg; Experiment type: Unknown; Subject type: Healthy. Subjects: 24; recordings: 117; 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/ds005241 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005241

Examples

>>> from eegdash.dataset import DS005241
>>> dataset = DS005241(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