ON003380: eeg dataset, 1 subjects#
Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig
Citation: Martin G. Frasch, Bernd Walter, Chrstophe L. Herry, Reinhard Bauer (20). Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig. 10.82901/nemar.on003380
1-participant EEG dataset — Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON003380
dataset = ON003380(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON003380(cache_dir="./data", subject="01")
Advanced query
dataset = ON003380(
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{on003380,
title = {Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig},
author = {Martin G. Frasch and Bernd Walter and Chrstophe L. Herry and Reinhard Bauer},
doi = {10.82901/nemar.on003380},
url = {https://doi.org/10.82901/nemar.on003380},
}
About This Dataset#
This sedation, ischemia, recovery experiment contains 11 animals (juvenile pigs).
Animals were surgically instrumented, and then monitored under sedation states 1-5 (isoflurane, fentanyl, propofol), followed by 1 or 2 episodes of gradual ischemia (states 6 and 8) and recovery (recovery 1 = state 7, between state 6 and 8; recovery 2, after state 8, corresponding to states 9-12).
Two crude groups are indicated: 1) sedation - animals had no ischemia and 2) ischemia - animals had sedation, followed by ischemia episodes and followed by recovery.
The scientific article (see Reference) contains all methodological details. - Martin Frasch and Reinhard Bauer, October 2, 2020
PS. Sub-12 folder is to be ignored. It was added to satisfy the BIDS validation algorithm.
Cohort#
Dataset Statistics#
Channel counts: 9 ch (n=1 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-12
Showing one representative recording out of
1 subjects and 1 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Martin G. Frasch, Bernd Walter, Chrstophe L. Herry, Reinhard Bauer |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on003380,
title = {Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig},
author = {Martin G. Frasch and Bernd Walter and Chrstophe L. Herry and Reinhard Bauer},
doi = {10.82901/nemar.on003380},
url = {https://doi.org/10.82901/nemar.on003380},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON003380(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig
- Study:
on003380(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON003380,nan.Modality:
eeg. Subjects: 1; recordings: 1; tasks: 0.- 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/on003380 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on003380 DOI: https://doi.org/10.82901/nemar.on003380
Examples
>>> from eegdash.dataset import ON003380 >>> dataset = ON003380(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 on003380 to reproduce the tutorial on this dataset.
Citation
Martin G. Frasch, Bernd Walter, Chrstophe L. Herry, Reinhard Bauer (20). Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig. 10.82901/nemar.on003380
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on003380.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset