DS004998: meg dataset, 20 subjects#
Exploring the electrophysiology of Parkinson’s disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.
Citation: Fayed Rassoulou, Alexandra Steina, Christian J. Hartmann, Jan Vesper, Markus Butz, Alfons Schnitzler, Jan Hirschmann (2024). Exploring the electrophysiology of Parkinson’s disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.. 10.18112/openneuro.ds004998.v1.2.2
20-participant MEG dataset — Exploring the electrophysiology of Parkinson's disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus..
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004998
dataset = DS004998(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004998(cache_dir="./data", subject="01")
Advanced query
dataset = DS004998(
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{ds004998,
title = {Exploring the electrophysiology of Parkinson's disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.},
author = {Fayed Rassoulou and Alexandra Steina and Christian J. Hartmann and Jan Vesper and Markus Butz and Alfons Schnitzler and Jan Hirschmann},
doi = {10.18112/openneuro.ds004998.v1.2.2},
url = {https://doi.org/10.18112/openneuro.ds004998.v1.2.2},
}
About This Dataset#
This dataset contains data from externalized DBS patients undergoing simultaneous MEG - STN LFP recordings with (MedOn) and without (MedOn) dopaminergic medication. It has two movement conditions: 1) 5 min of rest followed by static forearm extension (hold) and 2) 5 min of rest followed by self-paced fist-clenching (move). The movement parts contain pauses. Some patients were recorded in resting-state only (rest). The project aimed to understand the neurophysiology of basal ganglia-cortex loops and its modulation by movement and medication.
Code for quickly start is available here:
References
Rassoulou, F., Steina, A., Hartmann, C. J., Vesper, J., Butz, M., Schnitzler, A., & Hirschmann, J. (2024). Exploring the electrophysiology of Parkinson’s disease with magnetoencephalography and deep brain recordings. Scientific data, 11(1), 889. https://doi.org/10.1038/s41597-024-03768-1 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
Cohort#
Dataset Statistics#
Age distribution by gender (n=20, range 48–76 yr, mean 63.0 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 2000.0 Hz (n=85 recordings)
Total recording duration: 10 h 45 min
Signal · Electrodes & live trace#
Live trace viewer — sub-jyC0j3 · ses-PeriOp · task-HoldR · run-1
Showing one representative recording out of
20 subjects and 145 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 |
Exploring the electrophysiology of Parkinson’s disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus. |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2024 |
Authors |
Fayed Rassoulou, Alexandra Steina, Christian J. Hartmann, Jan Vesper, Markus Butz, Alfons Schnitzler, Jan Hirschmann |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004998,
title = {Exploring the electrophysiology of Parkinson's disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.},
author = {Fayed Rassoulou and Alexandra Steina and Christian J. Hartmann and Jan Vesper and Markus Butz and Alfons Schnitzler and Jan Hirschmann},
doi = {10.18112/openneuro.ds004998.v1.2.2},
url = {https://doi.org/10.18112/openneuro.ds004998.v1.2.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004998 · Rassoulou2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004998(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Exploring the electrophysiology of Parkinson’s disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.
- Study:
ds004998(OpenNeuro)- Author (year):
Rassoulou2024- Canonical:
—
Also importable as:
DS004998,Rassoulou2024.Modality:
meg; Experiment type:Motor; Subject type:Parkinson's. Subjects: 20; recordings: 145; tasks: 6.- 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/ds004998 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004998 DOI: https://doi.org/10.18112/openneuro.ds004998.v1.2.2 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004998 >>> dataset = DS004998(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/ds004998").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 ds004998 to reproduce the tutorial on this dataset.
Citation
Fayed Rassoulou, Alexandra Steina, Christian J. Hartmann, Jan Vesper, Markus Butz, … (2024). Exploring the electrophysiology of Parkinson's disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.. 10.18112/openneuro.ds004998.v1.2.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004998.v1.2.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset