NM000159: emg dataset, 16 subjects#
MUniverse Avrillon et al 2024
Citation: Simon Avrillon, Francois Hug, Roger M. Enoka, Arnault H. Caillet, Dario Farina (20). MUniverse Avrillon et al 2024. https://doi.org/10.7910/DVN/L9OQY7
16-participant EMG dataset — MUniverse Avrillon et al 2024.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000159
dataset = NM000159(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000159(cache_dir="./data", subject="01")
Advanced query
dataset = NM000159(
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{nm000159,
title = {MUniverse Avrillon et al 2024},
author = {Simon Avrillon and Francois Hug and Roger M. Enoka and Arnault H. Caillet and Dario Farina},
doi = {https://doi.org/10.7910/DVN/L9OQY7},
url = {https://doi.org/https://doi.org/10.7910/DVN/L9OQY7},
}
About This Dataset#
BIDS-formatted version of the HDsEMG dataset published in *`Avrillon et al. 2024 <https://doi.org/10.7554/eLife.97085.3>`__*.
Two experimental sessions consisted of either a series of submaximal (10-80 percent MVC)
isometric ankle dorsiflexions or isometric knee extensions. EMG signals were recorded from either the tibialis anterior (TA) or the vastus lateralis (VL) muscles using four arrays of 64 surface electrodes for a total of 256 electrodes.
Avrillon et al 2024: HDsEMG recordings
Population
16 young individuals volunteered to participate either in the experiment on the tibialis anterior (n=8; age: 27 +/- 3) or on the vastus lateralis (n=8; age: 27 +/- 10).
Electrode placement
Surface EMG signals were recorded from the TA or the VL using 4 two-dimensional arrays of
View full README
Avrillon et al 2024: HDsEMG recordings
Population
16 young individuals volunteered to participate either in the experiment on the tibialis anterior (n=8; age: 27 +/- 3) or on the vastus lateralis (n=8; age: 27 +/- 10).
Electrode placement
Surface EMG signals were recorded from the TA or the VL using 4 two-dimensional arrays of 64 electrodes (GR04MM1305 for the TA; GR08MM1305 for the VL, 13×5 gold-coated electrodes with one electrode absent on a corner; interelectrode distance: 4 and 8 mm, respectively; OT Bioelettronica, Italy).
The grids were positioned over the muscle bellies to cover the largest surface while staying away from the boundaries of the muscle identified by manual palpation. Before placing the electrodes, the skin was shaved and cleaned with an abrasive pad and water. A biadhesive foam layer was used to hold each array of electrodes onto the skin, and conductive paste filled the cavities of the adhesive layers to make skin-electrode contact.
Tibialis anterior: ankle dorsiflexions
For the session of ankle dorsiflexions, participants sat on a massage table with the hips flexed at 45 degree, 0 degree being the hip neutral position, and the knees fully extended.
The foot of the dominant leg (right in all participants) was fixed onto the pedal of an ankle dynamometer (OT Bioelettronica, Turin, Italy) positioned at 30 degree in the plantarflexion direction, 0 degree being the foot perpendicular to the shank. The thigh and the foot were fixed with inextensible Velcro straps. Force signals were recorded with a load cell (CCT Transducer s.a.s, Turin, Italy) connected in-series to the pedal using the same acquisition system as for the EMG recordings (EMG-Quattrocento; OT Bioelettronica, Italy).
Vastus lateralis: knee extensions
For the session of knee extensions, participants sat on an instrumented chair with the hips flexed at 85 degree, 0 degree being the hip neutral position, and the knees flexed at 85 degree, 0 degree being the knees fully extended. The torso and the thighs were fixed to the chair with Velcro straps and the tibia were positioned against a rigid resistance connected to force sensors (Metitur, Jyvaskyla, Finland). The force signals were recorded using the same acquisition system as for the EMG recordings.
Coordinate systems
All electrode coordinates (reported in mm) have been converted to a common reference frame corresponding to the first EMG-array (space-grid1).
The positions of the reference and ground electrodes are reported in a seperate coordinate system (space-lowerLeg) reported in percent of the lower leg length (knee-to-ankle).
Missing data
Contraction intensities 50, 60 and 70 % MVC are missing for subject 15.
Conversion
The dataset has been converted semi-automatically using the *MUniverse* software.
See dataset_description.json for further details.
Cohort#
Dataset Statistics#
Channel counts: 258 ch (n=124 recordings)
Sampling frequencies: 2048.0 Hz (n=124 recordings)
Total recording duration: 1 h 33 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-isometric80percentmvc · run-01
Showing one representative recording out of
16 subjects and 124 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _emg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?emg=<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 |
MUniverse Avrillon et al 2024 |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Simon Avrillon, Francois Hug, Roger M. Enoka, Arnault H. Caillet, Dario Farina |
License |
CC0 BY 4.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000159,
title = {MUniverse Avrillon et al 2024},
author = {Simon Avrillon and Francois Hug and Roger M. Enoka and Arnault H. Caillet and Dario Farina},
doi = {https://doi.org/10.7910/DVN/L9OQY7},
url = {https://doi.org/https://doi.org/10.7910/DVN/L9OQY7},
}
API Reference#
eegdash.datasetEEGDashDatasetNM000159 · Avrillon2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.NM000159(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
MUniverse Avrillon et al 2024
- Study:
nm000159(NeMAR)- Author (year):
Avrillon2024- Canonical:
—
Also importable as:
NM000159,Avrillon2024.Modality:
emg. Subjects: 16; recordings: 124; tasks: 8.- 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/nm000159 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000159 DOI: https://doi.org/https://doi.org/10.7910/DVN/L9OQY7
Examples
>>> from eegdash.dataset import NM000159 >>> dataset = NM000159(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.pytorch
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 nm000159 to reproduce the tutorial on this dataset.
Citation
Simon Avrillon, Francois Hug, Roger M. Enoka, Arnault H. Caillet, Dario Farina (20). MUniverse Avrillon et al 2024. https://doi.org/10.7910/DVN/L9OQY7
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: https://doi.org/10.7910/DVN/L9OQY7.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset