DS004388: eeg dataset, 40 subjects#

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

Access recordings and metadata through EEGDash.

Citation: Birgit Nierula, Tilman Stephani, Merve Kaptan, André Moruaux, Burkhard Maess, Gabriel Curio, Vadim V. Nikulin, Falk Eippert (2023). Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation. 10.18112/openneuro.ds004388.v1.0.0

Modality: eeg Subjects: 40 Recordings: 399 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

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},
}

About This Dataset#

Description

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.

Citing this dataset

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

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.

Dataset Information#

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	2023
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

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: 40
Recordings: 399
Tasks: 3

Channels & sampling rate

Channels: 115 (319), 114 (80)
Sampling rate (Hz): 10000.0
Duration (hours): 43.48990325

Electrode Layout#

Electrode layout — EEG · 38 sensors — 38 channels

Dataset Statistics#

Age distribution (n=40, range 19–33 yr)

15202530

Sex distribution

21

19

Female Male Total: 40

Channel counts (ch)

114115

Sampling frequencies: 10000.0 Hz (n=399 recordings)

Total recording duration: 43 h

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 DS004388 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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)