EEGdash›OpenNeuro›DS004388

Iss. 4388 · 40 subjects · 399 recordings · CC0

Dataset Brief · Somatosensory evoked potentials in the human spinal cord to m…

DS004388: eeg dataset, 40 subjects#

Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation

Citation: Birgit Nierula, Tilman Stephani, Merve Kaptan, André Moruaux, Burkhard Maess, Gabriel Curio, Vadim V. Nikulin, Falk Eippert (—). Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation. 10.18112/openneuro.ds004388.v1.0.0

40-participant EEG dataset — Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation.

Data & curation Birgit Nierula · Tilman Stephani · Merve Kaptan · André Moruaux · Burkhard Maess · Gabriel Curio · …
Funding n/a

EEG · 115 (319), 114 (80) ch10000 HzBIDS 1.6.03 tasksHealthyTactilePerception

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 DS004388

dataset = DS004388(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 = DS004388(cache_dir="./data", subject="01")

Advanced query

dataset = DS004388(
    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{ds004388,
  title = {Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation},
  author = {Birgit Nierula and Tilman Stephani and Merve Kaptan and André Moruaux and Burkhard Maess and Gabriel Curio and Vadim V. Nikulin and Falk Eippert},
  doi = {10.18112/openneuro.ds004388.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004388.v1.0.0},
}

§ 02Study · The README

About This Dataset#

This is a data set consisting of simultaneous electroencephalography (EEG), electrospinography (ESG), electroneurography (ENG), and electromyography (EMG) recordings from 40 participants. There were four different recording conditions: i) resting state with eyes open, ii) mixed median nerve stimulation (arm nerve), iii) mixed tibial nerve stimulation (leg nerve), and iv) alternating mixed median or tibial nerve stimulation. For each participant, there is i) the simultaneous EEG-ESG-ENG-EMG-recording which also includes electrocardiographic and respiratory signals, ii) ESG electrode positions. For a detailed description please see the following article: XXX. This study was pre-registered on OSF: https://osf.io/sgptzt.

Should you make use of this data set in any publication, please cite the following article: XXXX

Description

License

This data set is made available under the Creative Commons CC0 license. For more information, see https://creativecommons.org/share-your-work/public-domain/cc0/

Data set

This data set is organized according to the Brain Imaging Data Structure specification. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/ Each participant’s data are in one subdirectory (e.g., ‘sub-001’), which contains the raw data in eeglab format.

Please note that the EEG channel Fz was referenced to i) the EEG reference (right mastoid, RM, channel name: Fz) and ii) the ESG reference (6th thoracic vertebra, TH6, channel name: Fz-TH6). Should you have any questions about this data set, please contact nierula@cbs.mpg.de or eippert@cbs.mpg.de.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=40, range 19–33 yr, mean 25.7 yr)

15202530

Female · 21Male · 19

Sex composition

Female
21
Male
19
F : M ratio
1.11 : 1

52% female · n = 40 subjects with reported sex.

Channel counts (ch)

114115

Sampling frequencies: 10000.0 Hz (n=399 recordings)

Total recording duration: 43 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 115 (319), 114 (80) ch · EEG · 10000 Hz · 40 subjects, 399 recordings

Live trace viewer — sub-021 · task-median · run-04

Showing one representative recording out of 40 subjects and 399 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 · 38 sensors — 38 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

§ 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.DS004388(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Nierula2023_Somatosensory

Canonical—

Importable asDS004388 · Nierula2023_Somatosensory

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

class eegdash.dataset.DS004388(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation

Study:: ds004388 (OpenNeuro)
Author (year):: Nierula2023_Somatosensory
Canonical:: —

Also importable as: DS004388, Nierula2023_Somatosensory.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 40; recordings: 399; tasks: 3.

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/ds004388 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004388 DOI: https://doi.org/10.18112/openneuro.ds004388.v1.0.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004388
>>> dataset = DS004388(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/ds004388 · pull with datasets.load_dataset("EEGDash/ds004388").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004388.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 ds004388 to reproduce the tutorial on this dataset.

Citation

Birgit Nierula, Tilman Stephani, Merve Kaptan, André Moruaux, Burkhard Maess, … (n.d.). Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation. 10.18112/openneuro.ds004388.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.ds004388.v1.0.0.

Related & sibling datasets

DS004389EEG · 26 subjSame authors DS005307EEG · 7 subjSame authors DS006374EEG · 36 subj DS005285EEG · 29 subj DS005286EEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.6.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004388.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004388`
Title	Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation
Author (year)	`Nierula2023_Somatosensory`
Canonical	—
Importable as	`DS004388`, `Nierula2023_Somatosensory`
Year	—
Authors	Birgit Nierula, Tilman Stephani, Merve Kaptan, André Moruaux, Burkhard Maess, Gabriel Curio, Vadim V. Nikulin, Falk Eippert
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004388.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS004388: eeg dataset, 40 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#