DS004389: eeg dataset, 26 subjects#
Somatosensory evoked potentials in the human spinal cord to mixed and sensory 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 and sensory nerve stimulation. 10.18112/openneuro.ds004389.v1.0.0
26-participant EEG dataset — Somatosensory evoked potentials in the human spinal cord to mixed and sensory nerve stimulation.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004389
dataset = DS004389(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004389(cache_dir="./data", subject="01")
Advanced query
dataset = DS004389(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds004389,
title = {Somatosensory evoked potentials in the human spinal cord to mixed and sensory 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.ds004389.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004389.v1.0.0},
}
About This Dataset#
This is a data set consisting of simultaneous electroencephalography (EEG), electrospinography (ESG), electroneurography (ENG), and electromyography (EMG) recordings from 26 participants. There were nine different recording conditions: i) resting state with eyes open, ii) mixed median nerve stimulation (arm nerve), iii) mixed tibial nerve stimulation (leg nerve), iv) sensory nerve stimulation of the index finger, v) sensory nerve stimulation of the middle finger, vi) simultaneous senory nerve stimulation of the index and middle finger, vii) sensory nerve stimulation to the first toe, viii) sensory nerve stimulation to the second toe, ix) simultaneous senory nerve stimulation to the first and second toe. 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/mjdha.
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=26, range 19–35 yr, mean 24.1 yr)
Sex composition
Channel counts: 90 ch (n=260 recordings)
Sampling frequencies: 10000.0 Hz (n=260 recordings)
Total recording duration: 30 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-medianmixed · run-07
Showing one representative recording out of
26 subjects and 260 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
Somatosensory evoked potentials in the human spinal cord to mixed and sensory nerve stimulation |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Birgit Nierula, Tilman Stephani, Merve Kaptan, André Moruaux, Burkhard Maess, Gabriel Curio, Vadim V. Nikulin, Falk Eippert |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004389,
title = {Somatosensory evoked potentials in the human spinal cord to mixed and sensory 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.ds004389.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004389.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004389 · Nierula2023_Somatosensory_evokedeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004389(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Somatosensory evoked potentials in the human spinal cord to mixed and sensory nerve stimulation
- Study:
ds004389(OpenNeuro)- Author (year):
Nierula2023_Somatosensory_evoked- Canonical:
—
Also importable as:
DS004389,Nierula2023_Somatosensory_evoked.Modality:
eeg; Experiment type:Perception; Subject type:Healthy. Subjects: 26; recordings: 260; tasks: 4.- 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds004389 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004389 DOI: https://doi.org/10.18112/openneuro.ds004389.v1.0.0 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS004389 >>> dataset = DS004389(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004389").huggingface
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 ds004389 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 and sensory nerve stimulation. 10.18112/openneuro.ds004389.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.ds004389.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset