DS004229: meg dataset, 2 subjects#
amnoise
Citation: Maria Mittag, Eric Larson, Maggie Clarke, Samu Taulu, Patricia K. Kuhl (2021). amnoise. 10.18112/openneuro.ds004229.v1.0.3
2-participant MEG dataset — amnoise.
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#
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
ILABS amnoise MEG BIDS dataset
Cohort#
Dataset Statistics#
Age distribution by gender (n=1, range 15–15 yr, mean 15.0 yr)
Channel counts: 332 ch (n=2 recordings)
Sampling frequencies: 1200.0 Hz (n=2 recordings)
Total recording duration: 19 min
Signal · Electrodes & live trace#
Live trace viewer — sub-102 · task-amnoise
Showing one representative recording out of
2 subjects and 3 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _meg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?meg=<url>) to inspect it.
Electrode layout — MEG · 306 sensors — 306 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
amnoise |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2021 |
Authors |
Maria Mittag, Eric Larson, Maggie Clarke, Samu Taulu, Patricia K. Kuhl |
License |
CC0 |
Citation / DOI |
|
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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004229 · Mittag2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004229(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
amnoise
- Study:
ds004229(OpenNeuro)- Author (year):
Mittag2022- Canonical:
—
Also importable as:
DS004229,Mittag2022.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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds004229.v1.0.3 NEMAR citation count: 0
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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004229").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds004229 to reproduce the tutorial on this dataset.
Citation
Maria Mittag, Eric Larson, Maggie Clarke, Samu Taulu, Patricia K. Kuhl (2021). amnoise. 10.18112/openneuro.ds004229.v1.0.3
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004229.v1.0.3.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset