ON003195: eeg dataset, 10 subjects#
Placebo Neuroepo multisession
Citation: Maria Luisa Bringas Vega, Lilia Morales Chacon, Ivonne Pedroso Ibanez (2019). Placebo Neuroepo multisession. 10.82901/nemar.on003195
10-participant EEG dataset — Placebo Neuroepo multisession.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON003195
dataset = ON003195(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON003195(cache_dir="./data", subject="01")
Advanced query
dataset = ON003195(
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{on003195,
title = {Placebo Neuroepo multisession},
author = {Maria Luisa Bringas Vega and Lilia Morales Chacon and Ivonne Pedroso Ibanez},
doi = {10.82901/nemar.on003195},
url = {https://doi.org/10.82901/nemar.on003195},
}
About This Dataset#
The quest for neuroprotection in Parkinson’s disease (PD) has been for new compounds to slow disease progression and stable and non-invasive biomarkers to document their benefits. Neuroepo, a new formulation of EPO with low content of sialic acid reported good results in animal model and tolerance in healthy participants and PD patients.
In a double-blind randomized placebo (https://clinicaltrials.gov/ number NCT04110678) twenty-five PD patients were assigned randomly to Neuroepo (n=15) or placebo (n=10) groups we reported the tolerance of the drug. We recorded resting-state EEG before and six months after the administration of the drug. The qualitative analysis of the abnormalities of the EEG was evaluated by two experts using a Likert-type scale and a multivariate item response theory (MIRT) approach was employed to stablish the differences between groups in the two times. The quantitative EEG (qEEG) analysis was performed at the sources looking for generators of the neural activity using software VARETA and co-registering the results using the Montreal Neurological Institute Atlas. The statistical analysis between the sources was conducted using a permutation test and later a contrast method using the surfstat software between groups and before vs after condition, with Bonferroni correction for multiple comparisons.
Here in this repository we placed the raw EEG in BIDS format (Pernet, C. R. et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci. data 6, 103 (2019). For the use of VARETA the qEEG program you can use (Bosch-Bayard, J. et al. A Quantitative EEG Toolbox for the MNI Neuroinformatics Ecosystem: Normative SPM of EEG Source Spectra. Front. Neuroinform. 14, (2020).) The EEG dataset from the different stages of processing can be requested to the authors.
Cohort#
Dataset Statistics#
Sex composition
Channel counts: 19 ch (n=20 recordings)
Sampling frequencies: 200.0 Hz (n=20 recordings)
Total recording duration: 4 h 39 min
Signal · Electrodes & live trace#
Live trace viewer — sub-PLAC02 · ses-placebo6m · task-taskplacebo6m
Showing one representative recording out of
10 subjects and 20 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 19 sensors — 19 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 |
Placebo Neuroepo multisession |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Maria Luisa Bringas Vega, Lilia Morales Chacon, Ivonne Pedroso Ibanez |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on003195,
title = {Placebo Neuroepo multisession},
author = {Maria Luisa Bringas Vega and Lilia Morales Chacon and Ivonne Pedroso Ibanez},
doi = {10.82901/nemar.on003195},
url = {https://doi.org/10.82901/nemar.on003195},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON003195(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Placebo Neuroepo multisession
- Study:
on003195(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON003195,nan.Modality:
eeg. Subjects: 10; recordings: 20; 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/on003195 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on003195 DOI: https://doi.org/10.82901/nemar.on003195
Examples
>>> from eegdash.dataset import ON003195 >>> dataset = ON003195(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.pytorch
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 on003195 to reproduce the tutorial on this dataset.
Citation
Maria Luisa Bringas Vega, Lilia Morales Chacon, Ivonne Pedroso Ibanez (2019). Placebo Neuroepo multisession. 10.82901/nemar.on003195
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on003195.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset