NM000159: emg dataset, 16 subjects#

MUniverse Avrillon et al 2024

Access recordings and metadata through EEGDash.

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

Modality: emg Subjects: 16 Recordings: 124 License: CC0 BY 4.0 Source: nemar

Metadata: Complete (100%)

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#

Avrillon et al 2024: HDsEMG recordings

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.

Population

View full README

Avrillon et al 2024: HDsEMG recordings

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.

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.

Dataset Information#

Dataset ID

NM000159

Title

MUniverse Avrillon et al 2024

Author (year)

Avrillon2024

Canonical

—

Importable as

NM000159, Avrillon2024

Year

20

Authors

Simon Avrillon, Francois Hug, Roger M. Enoka, Arnault H. Caillet, Dario Farina

License

CC0 BY 4.0

Citation / DOI

https://doi.org/10.7910/DVN/L9OQY7

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},
}

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: 16

  • Recordings: 124

  • Tasks: 8

Channels & sampling rate

  • Channels: 258

  • Sampling rate (Hz): 2048

  • Duration (hours): 1.5522222222222222

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 5.5 GB

  • File count: 124

  • Format: BIDS

License & citation
Provenance

API Reference#

Use the NM000159 class to access this dataset programmatically.

class eegdash.dataset.NM000159(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

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/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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#