EEGdash›OpenNeuro›DS004381

Iss. 4381 · 18 subjects · 437 recordings · CC0

Dataset Brief · Intraoperative EEG dataset during medianus-tibialis stimulati…

DS004381: eeg dataset, 18 subjects#

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

Citation: Giorgio Selmin, Vasileios Dimakopoulos, Niklaus Krayenbühl, Luca Regli, Johannes Sarnthein (20). Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates. 10.18112/openneuro.ds004381.v1.0.2

18-participant EEG dataset — Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates.

Data & curation Giorgio Selmin · Vasileios Dimakopoulos · Niklaus Krayenbühl · Luca Regli · Johannes Sarnthein
Year 20 · Distributed via OpenNeuro
Funding Swiss National Science Foundation, SNSF 204651 · Alexander S. Onassis Public Benefit Foundation Scholarship

EEG · 4 (333), 8 (47), 7 (26), 5 (26), 10 (5) ch20000 HzBIDS 1.4.0Task · sepRate4 sessionsSurgeryOtherOther

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
}

§ 02Study · The README

About This Dataset#

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).

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

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/)

View full README

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

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.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=18, range 4–87 yr, mean 43.4 yr)

051025303540505560657085

Female · 6Male · 12

Sex composition

Female
6
Male
12
F : M ratio
0.50 : 1

33% female · n = 18 subjects with reported sex.

Channel counts (ch)

457810

Sampling frequencies: 20000.0 Hz (n=437 recordings)

Total recording duration: 11 h 48 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 4 (333), 8 (47), 7 (26), 5 (26), 10 (5) ch · EEG · 20000 Hz · 18 subjects, 437 recordings

Live trace viewer — sub-13 · ses-04 · task-sepRate · run-07

Showing one representative recording out of 18 subjects and 437 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS004381(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Selmin2022

Canonical—

Importable asDS004381 · Selmin2022

Sourceeegdash/dataset/registry.py · [source ↗]

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

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

Study:: ds004381 (OpenNeuro)
Author (year):: Selmin2022
Canonical:: —

Also importable as: DS004381, Selmin2022.

Modality: eeg. 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 DOI: https://doi.org/10.18112/openneuro.ds004381.v1.0.2 NEMAR citation count: 2

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: 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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds004381 · pull with datasets.load_dataset("EEGDash/ds004381").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004381.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds004381 to reproduce the tutorial on this dataset.

Citation

Giorgio Selmin, Vasileios Dimakopoulos, Niklaus Krayenbühl, Luca Regli, Johannes Sarnthein (20). Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates. 10.18112/openneuro.ds004381.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds004381.v1.0.2.

Related & sibling datasets

DS004024EEG · 13 subj DS003626EEG · 10 subj DS007541EEG · 32 subj DS007509EEG · 69 subj DS007721EEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.4.0

Sidecars

channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004381.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004381`
Title	Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates
Author (year)	`Selmin2022`
Canonical	—
Importable as	`DS004381`, `Selmin2022`
Year	20
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

DS004381: eeg dataset, 18 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#