DS007864: eeg dataset, 45 subjects#
Neuroepo multisession Phase II and III
Citation: Maria Luisa Bringas Vega, Lilia Morales Chacon, Ivonne Pedroso Ibanez (20). Neuroepo multisession Phase II and III. 10.18112/openneuro.ds007864.v1.0.0
45-participant EEG dataset — Neuroepo multisession Phase II and III.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS007864
dataset = DS007864(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS007864(cache_dir="./data", subject="01")
Advanced query
dataset = DS007864(
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{ds007864,
title = {Neuroepo multisession Phase II and III},
author = {Maria Luisa Bringas Vega and Lilia Morales Chacon and Ivonne Pedroso Ibanez},
doi = {10.18112/openneuro.ds007864.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007864.v1.0.0},
}
About This Dataset#
Erythropoietin (EPO) and EPO-derived formulations have been proposed as neuroprotective and neuromodulatory agents with
pleiotropic actions that extend beyond erythropoiesis, including anti-inflammatory and anti-oxidative effects, modulation of apoptosis- related signaling, and support of cellular resilience under stress (Maiese et al., 2005; Rey et al., 2019). In Parkinson’s disease-relevant contexts, EPO has been linked to mechanisms involving mitochondrial metabolism and pathways relevant to dopaminergic vulnerability, suggesting a plausible route by which exposure could influence systems-level brain function (Rey et al., 2021). At the molecular level, EPO-related transcriptional responses in the brain have been shown to engage synaptic plasticity-associated gene programs, supporting the interpretation that treatment exposure could be accompanied by functional reorganization rather than solely symptomatic modulation (Mengozzi et al., 2012). Clinically and translationally, intranasal NeuroEPO has been evaluated for short-term tolerance in Parkinson’s disease, providing practical context for the formulation and administration route used in patient studies (Garcia-Llano et al., 2021). Moreover, prior work has reported that NeuroEPO-related cognitive effects in Parkinson’s disease patients can be statistically mediated by EEG source activity, providing precedent for an exposure ? EEG mediator ? outcome framework in this setting (Bringas Vega et al., 2022).
Participants were enrolled in a randomized, placebo-controlled study. NeuroEPO exposure was administered according to the
intervention protocol (Cuban Public Registry of Clinical Trials: RPCEC00000233-En; https://rpcec.sld.cu/en/trials/RPCEC00000233 -En ), while placebo participants received identical procedures without active compound. Cumulative treatment exposure was quantified as total NeuroEPO dose; placebo participants were coded as dose = 0. Clinical and EEG assessments were conducted at baseline (pre-intervention) and after 9 months of completion of the intervention period (post-intervention).
Baseline demographic and clinical characteristics were summarized for the NeuroEPO and placebo groups. Baseline variables included age (years), education (years), and study-coded measures of disease duration/progression and motor severity available in the source dataset. The motor performance was assessed by two certified neurologists using the Unified Parkinson Disease Rating Scale MDS UPDRS III item scores collected at pre and post-intervention visits (Goetz et al., 2008), where the severity variable was recorded as an ordinal 0-4 grade, with lower values indicating better motor status, and was used for baseline description only.
Cohort#
Dataset Statistics#
Sex composition
Channel counts (ch)
Sampling frequencies: 200.0 Hz (n=84 recordings)
Total recording duration: 17 h 58 min
Signal · Electrodes & live trace#
Live trace viewer — sub-NeuroEpo86 · ses-2 · task-resteyesclosed
Showing one representative recording out of
45 subjects and 84 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 |
Neuroepo multisession Phase II and III |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
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{ds007864,
title = {Neuroepo multisession Phase II and III},
author = {Maria Luisa Bringas Vega and Lilia Morales Chacon and Ivonne Pedroso Ibanez},
doi = {10.18112/openneuro.ds007864.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds007864.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS007864(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Neuroepo multisession Phase II and III
- Study:
ds007864(OpenNeuro)- Author (year):
nan- Canonical:
—
Also importable as:
DS007864,nan.Modality:
eeg. Subjects: 45; recordings: 84; tasks: 1.- 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/ds007864 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007864 DOI: https://doi.org/10.18112/openneuro.ds007864.v1.0.0
Examples
>>> from eegdash.dataset import DS007864 >>> dataset = DS007864(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 ds007864 to reproduce the tutorial on this dataset.
Citation
Maria Luisa Bringas Vega, Lilia Morales Chacon, Ivonne Pedroso Ibanez (20). Neuroepo multisession Phase II and III. 10.18112/openneuro.ds007864.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds007864.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset