DS003380: eeg dataset, 1 subjects#

Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig

Access recordings and metadata through EEGDash.

Citation: Martin G. Frasch, Bernd Walter, Chrstophe L. Herry, Reinhard Bauer (2020). Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig. 10.18112/openneuro.ds003380.v1.0.0

Modality: eeg Subjects: 1 Recordings: 5 License: CC0 Source: openneuro

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003380

dataset = DS003380(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 = DS003380(cache_dir="./data", subject="01")

Advanced query

dataset = DS003380(
    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{ds003380,
  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 = {10.18112/openneuro.ds003380.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003380.v1.0.0},
}

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.

Dataset Information#

Dataset ID	`DS003380`
Title	Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig
Author (year)	`Frasch2020`
Canonical	—
Importable as	`DS003380`, `Frasch2020`
Year	2020
Authors	Martin G. Frasch, Bernd Walter, Chrstophe L. Herry, Reinhard Bauer
License	CC0
Citation / DOI	10.18112/openneuro.ds003380.v1.0.0
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: 1
Recordings: 5
Tasks: —

Channels & sampling rate

Channels: 16
Sampling rate (Hz): Varies
Duration (hours): 0.0894444444444444

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Channel counts: 16 ch (n=1 recordings)

Total recording duration: 5 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS003380 class to access this dataset programmatically.

class eegdash.dataset.DS003380(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig

Study:: ds003380 (OpenNeuro)
Author (year):: Frasch2020
Canonical:: —

Also importable as: DS003380, Frasch2020.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 1; recordings: 5; 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

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/ds003380 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003380 DOI: https://doi.org/10.18112/openneuro.ds003380.v1.0.0

Examples

>>> from eegdash.dataset import DS003380
>>> dataset = DS003380(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)