DS005692: eeg dataset, 30 subjects#
SpinalExpect_NonInvasive
Citation: Max-Philipp Stenner, Cindy Marquez Nossa, Tino Zaehle, Elena Azanon, Hans-Jochen Heinze, Matthias Deliano, Lars Buentjen (2022). SpinalExpect_NonInvasive. 10.18112/openneuro.ds005692.v1.0.0
30-participant EEG dataset — SpinalExpect_NonInvasive.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005692
dataset = DS005692(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005692(cache_dir="./data", subject="01")
Advanced query
dataset = DS005692(
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{ds005692,
title = {SpinalExpect_NonInvasive},
author = {Max-Philipp Stenner and Cindy Marquez Nossa and Tino Zaehle and Elena Azanon and Hans-Jochen Heinze and Matthias Deliano and Lars Buentjen},
doi = {10.18112/openneuro.ds005692.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005692.v1.0.0},
}
About This Dataset#
Contact person:
Max-Philipp Stenner, email: max-philipp.stenner@med.ovgu.de or max-philipp.stenner@lin-magdeburg.de, ORCID: 0000-0002-3694-1887
Brief overview of the tasks in the experiment:
The goal was to reveal whether temporal expectation influences initial sensory processing in the human spinal cord. In each trial, subjects (n=30, healthy, young) received at least one electric median nerve stimulation on the left wrist. Their task was to silently count in how many (rare) trials in each block (~5 min) there were two median nerve stimuli in rapid succession (80 ms ISI), and to report that number at the end of each block. In each trial, the median nerve stimulation was preceded by an auditory cue. In alternating blocks, the time interval between the auditory cue and the (first) median nerve stimulation was either fixed at 1100 ms, or varied randomly (uniform distribution) between 100 and 2100 ms. Each subject (except for sub-24) completed two days of testing. Whether a session began with a constant-interval block or variable-interval block was counterbalanced across subjects.
Montage:
Adapted from/extended beyond Chander, B. S., Deliano, M., Azañón, E., Büntjen, L., & Stenner, M. P. (2022). Non-invasive recording of high-frequency signals from the human spinal cord. NeuroImage, 253, 119050, as follows. Electrodes were placed in three rings around the neck. “Sr” in the channel names stands for “spinal ring”. There was a caudal, middle, and cranial ring of electrodes. The middle ring was connecting a point above the spinous process of the sixth cervical vertebra (SrC6), and a point above the thyroid cartilage (“SrF”). Between these two electrodes, the remaining six electrodes for the middle ring were evenly spaced around the neck. “L” in channel names means left, “R” means right, “B” means “back”, “F” means front, and “M” means middle. For the caudal and cranial rings, electrodes were placed approximately 2 cm below (“_b”) and 2 cm above (“_a”) the corresponding electrodes of the middle ring, respectively.
Events:
There are three events of interest. “S1” is a keypress of the experimenter, to advance the task upon breaks. “S1” can therefore be used to identify the start of each new block. “S70” corresponds to the auditory cue. “S130” corresponds to median nerve stimulation. “S99” is a “placeholder” that corresponds to no stimulus at all. The placeholder was used to enable “double stimuli”, i.e., a rapid succession of two median nerve stimulations with an ISI of 80 ms. Most trials follow a sequence of “S70” - “S130” - “S99”. Deviant trials have “S70” - “S130” - “S130”.
Cohort#
Dataset Statistics#
Age distribution by gender (n=30, range 20–32 yr, mean 26.0 yr)
Sex composition
Channel counts: 25 ch (n=59 recordings)
Sampling frequencies: 5000.0 Hz (n=59 recordings)
Total recording duration: 112 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-2 · task-countingDeviants
Showing one representative recording out of
30 subjects and 59 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
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 |
SpinalExpect_NonInvasive |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2022 |
Authors |
Max-Philipp Stenner, Cindy Marquez Nossa, Tino Zaehle, Elena Azanon, Hans-Jochen Heinze, Matthias Deliano, Lars Buentjen |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005692,
title = {SpinalExpect_NonInvasive},
author = {Max-Philipp Stenner and Cindy Marquez Nossa and Tino Zaehle and Elena Azanon and Hans-Jochen Heinze and Matthias Deliano and Lars Buentjen},
doi = {10.18112/openneuro.ds005692.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005692.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005692 · Stenner2024_SpinalExpect_NonInvasiveeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005692(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
SpinalExpect_NonInvasive
- Study:
ds005692(OpenNeuro)- Author (year):
Stenner2024_SpinalExpect_NonInvasive- Canonical:
—
Also importable as:
DS005692,Stenner2024_SpinalExpect_NonInvasive.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 30; recordings: 59; 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
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/ds005692 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005692 DOI: https://doi.org/10.18112/openneuro.ds005692.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS005692 >>> dataset = DS005692(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/ds005692").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 ds005692 to reproduce the tutorial on this dataset.
Citation
Max-Philipp Stenner, Cindy Marquez Nossa, Tino Zaehle, Elena Azanon, Hans-Jochen Heinze, … (2022). SpinalExpect_NonInvasive. 10.18112/openneuro.ds005692.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.ds005692.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset