DS004381#

Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates

Access recordings and metadata through EEGDash.

Citation: Giorgio Selmin, Vasileios Dimakopoulos, Niklaus Krayenbühl, Luca Regli, Johannes Sarnthein (2022). Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates. 10.18112/openneuro.ds004381.v1.0.2

Modality: eeg Subjects: 18 Recordings: 1401 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004381

dataset = DS004381(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 = DS004381(cache_dir="./data", subject="01")

Advanced query

dataset = DS004381(
    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{ds004381,
  title = {Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates},
  author = {Giorgio Selmin and Vasileios Dimakopoulos and Niklaus Krayenbühl and Luca Regli and Johannes Sarnthein},
  doi = {10.18112/openneuro.ds004381.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004381.v1.0.2},
}

About This Dataset#

Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates

This dataset was obtained from the publication [1] wherein we varyied the stimulus repetition rate and recorded medianus and tibial nerve SEP. We randomly sampled a number of sweeps corresponding to recording durations up to 20 s and calculated the signal-to-noise ratio (SNR).

There are 14 adults subjects and 4 children subjects with continuous EEG data split in sessions (tibial left/right, medianus left/right) and runs (1 run for each stimulation rate). We also provide processed data (derivatives) for all the sessions. In total there are 34 medianus SEP and 32 tibial SEP sessions.

View full README

Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates

This dataset was obtained from the publication [1] wherein we varyied the stimulus repetition rate and recorded medianus and tibial nerve SEP. We randomly sampled a number of sweeps corresponding to recording durations up to 20 s and calculated the signal-to-noise ratio (SNR).

There are 14 adults subjects and 4 children subjects with continuous EEG data split in sessions (tibial left/right, medianus left/right) and runs (1 run for each stimulation rate). We also provide processed data (derivatives) for all the sessions. In total there are 34 medianus SEP and 32 tibial SEP sessions.

Repository structure

Main directory (SEP rate/)

Contains metadata files in the BIDS standard about the participants and the study. Folders are explained below.

Subfolders

*SEP rate/sub-**/ Contains folders for each subject, named sub-<subject number> and session information. *SEP rate/sub-**/ses-01/eeg Contains the raw eeg data in .edf format for each subject. Each *eeg.edf file contains EEG data from one stimulation rate (see scans.tsv column stimRate). Details about the channels are given in the corresponding .tsv file. * SEP rate/derivatives

Contains folders for each subject,named sub-<subject number> and session information that include processed data *SEP rate/derivatives/sub-**/ses-01/eeg/ Contains processed data for each subject.

Note from the paper

“The offline data processing used the continuous EEG that was recorded in parallel to the SEP recordings. Data analysis was performed with custom scripts in Matlab (www.mathworks.com). To detect the SEP stimulation artefact, we first filtered the EEG (high pass cutoff = 200 Hz) and performed local peak detection (minimum peak prominence between peaks = 30 ms, minimum peak width = 4 ms, samples = 0.2 ms). We used the times of the detected stimulus artifact as triggers to define sweeps with post-stimulus recording sweep length 50 ms for medianus SEP and 100 ms for tibial SEP. We resampled the data to sampling rate 1200 Hz before further processing. We classified sweeps with amplitude > 10 ÂµV as artefact-ridden and excluded them from further analysis.”

BIDS Conversion

bids-starter-kid and custom Matlab scripts were used to convert the dataset into BIDS format.

References

[1] Dimakopoulos V, Selmin G, Regli L, Sarnthein J, Optimization of signal-to-noise ratio in short-duration SEP recordings by variation of stimulation rate, Clinical Neurophysiology, 2023, ISSN 1388-2457, https://doi.org/10.1016/j.clinph.2023.03.008.

Dataset Information#

Dataset ID	`DS004381`
Title	Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates
Year	2022
Authors	Giorgio Selmin, Vasileios Dimakopoulos, Niklaus Krayenbühl, Luca Regli, Johannes Sarnthein
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004381.v1.0.2
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: 18
Recordings: 1401
Tasks: 1

Channels & sampling rate

Channels: 4 (666), 8 (94), 7 (52), 5 (52), 10 (10)
Sampling rate (Hz): 20000.0
Duration (hours): 0.0

API Reference#

Use the DS004381 class to access this dataset programmatically.

class eegdash.dataset.DS004381(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004381. Modality: eeg; Experiment type: Other; Subject type: Surgery. Subjects: 18; recordings: 437; 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

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/ds004381 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004381

Examples

>>> from eegdash.dataset import DS004381
>>> dataset = DS004381(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