DS003194#

Neuroepo multisession

Access recordings and metadata through EEGDash.

Citation: Maria Luisa Bringas Vega, Lilia Morales Chacon, Ivonne Pedroso Ibanez (2020). Neuroepo multisession. 10.18112/openneuro.ds003194.v1.0.4

Modality: eeg Subjects: 15 Recordings: 149 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003194

dataset = DS003194(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 = DS003194(cache_dir="./data", subject="01")

Advanced query

dataset = DS003194(
    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{ds003194,
  title = {Neuroepo multisession},
  author = {Maria Luisa Bringas Vega and Lilia Morales Chacon and Ivonne Pedroso Ibanez},
  doi = {10.18112/openneuro.ds003194.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds003194.v1.0.4},
}

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 establish 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.

Dataset Information#

Dataset ID	`DS003194`
Title	Neuroepo multisession
Year	2020
Authors	Maria Luisa Bringas Vega, Lilia Morales Chacon, Ivonne Pedroso Ibanez
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003194.v1.0.4
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: 15
Recordings: 149
Tasks: 2

Channels & sampling rate

Channels: 19 (52), 20 (4), 21 (2)
Sampling rate (Hz): 200.0
Duration (hours): 0.0

API Reference#

Use the DS003194 class to access this dataset programmatically.

class eegdash.dataset.DS003194(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003194. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. Subjects: 15; recordings: 29; 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/ds003194 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003194

Examples

>>> from eegdash.dataset import DS003194
>>> dataset = DS003194(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