NM000165: emg dataset, 1 subjects#

MUniverse Grison et al 2025

Access recordings and metadata through EEGDash.

Citation: Agnese Grison, Irene Mendez Guerra, Alexander Kenneth Clarke, Silvia Muceli, Jaime Ibanez Pereda, Dario Farina (20). MUniverse Grison et al 2025. https://doi.org/10.7910/DVN/ID1WNQ

Modality: emg Subjects: 1 Recordings: 10 License: CC0 BY 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000165

dataset = NM000165(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000165(cache_dir="./data", subject="01")

Advanced query

dataset = NM000165(
    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{nm000165,
  title = {MUniverse Grison et al 2025},
  author = {Agnese Grison and Irene Mendez Guerra and Alexander Kenneth Clarke and Silvia Muceli and Jaime Ibanez Pereda and Dario Farina},
  doi = {https://doi.org/10.7910/DVN/ID1WNQ},
  url = {https://doi.org/https://doi.org/10.7910/DVN/ID1WNQ},
}

About This Dataset#

Grison et al 2025: HDsEMG recordings

BIDS-formatted version of a HDsEMG dataset corresponding to *`Grison et al. 2025 <https://doi.org/10.1113/JP287913>`__*.

Overview

One healthy subject performed 10 submaximal (10 to 70 percent MVC) isometric ankle dorsiflexions. EMG signals were recorded from the right tibialis anterior using two arrays of 64 surface electrodes (4 mm interelectrode distance, 13x5 configuration)

View full README

Grison et al 2025: HDsEMG recordings

BIDS-formatted version of a HDsEMG dataset corresponding to *`Grison et al. 2025 <https://doi.org/10.1113/JP287913>`__*.

Overview

One healthy subject performed 10 submaximal (10 to 70 percent MVC) isometric ankle dorsiflexions. EMG signals were recorded from the right tibialis anterior using two arrays of 64 surface electrodes (4 mm interelectrode distance, 13x5 configuration) for a total of 128 electrodes.

Protocol description

The participant performed one, two, or three trapezoidal contractions (with repetitions being specified by the run labels) at 10, 15, 20, 25, 30, 35, 40, 50, 60, and 70 percent MVC with 120 s of rest in between, consisting of linear ramps up and down performed at 5 percent per second and a plateau maintained for 20 s up to 30 percent MVC, 15 s for 35 percent and 40 percent MVC, and 10 s from 50 percent to 70 percent MVC. The order of the contractions was randomized.

Set-up description

The participant sat on a chair with the hips flexed at 30 degrees, 0 degrees being the hip neutral position, and their knees fully extended. The foot of the dominant leg (right) was fixed onto the pedal of a commercial dynamometer (OT Bioelettronica) positioned at 30 degrees in the plantarflexion direction. Force signals were recorded with a load cell (CCT Transducer s.a.s.) connected in-series to the pedal using the same acquisition system as for the HD-EMG recordings.

Coordinate systems

All electrode coordinates (reported in mm) are reported in their respective grid coordinate system (space-grid1*and*space-grid2). Their relative positions as well as the positions of the reference and ground electrodes are reported in a separate coordinate system (space-lowerLeg) reported in percent of the lower leg length.

Labeled motor unit spike trains

Labeled motor unit spike trains were derived from concurrently recorded invasive EMG and curated by an experienced investigator (only available for *_run-01* of each trial).

Conversion

The dataset has been converted semi-automatically using the *MUniverse* software. See dataset_description.json for further details.

Dataset Information#

Dataset ID

NM000165

Title

MUniverse Grison et al 2025

Author (year)

Grison2025

Canonical

—

Importable as

NM000165, Grison2025

Year

20

Authors

Agnese Grison, Irene Mendez Guerra, Alexander Kenneth Clarke, Silvia Muceli, Jaime Ibanez Pereda, Dario Farina

License

CC0 BY 4.0

Citation / DOI

https://doi.org/10.7910/DVN/ID1WNQ

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000165,
  title = {MUniverse Grison et al 2025},
  author = {Agnese Grison and Irene Mendez Guerra and Alexander Kenneth Clarke and Silvia Muceli and Jaime Ibanez Pereda and Dario Farina},
  doi = {https://doi.org/10.7910/DVN/ID1WNQ},
  url = {https://doi.org/https://doi.org/10.7910/DVN/ID1WNQ},
}

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: 1

  • Recordings: 10

  • Tasks: 10

Channels & sampling rate

  • Channels: 131

  • Sampling rate (Hz): 10240

  • Duration (hours): 0.1491666666666666

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 1.3 GB

  • File count: 10

  • Format: BIDS

License & citation
Provenance

API Reference#

Use the NM000165 class to access this dataset programmatically.

class eegdash.dataset.NM000165(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

MUniverse Grison et al 2025

Study:

nm000165 (NeMAR)

Author (year):

Grison2025

Canonical:

—

Also importable as: NM000165, Grison2025.

Modality: emg. Subjects: 1; recordings: 10; tasks: 10.

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/nm000165 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000165 DOI: https://doi.org/https://doi.org/10.7910/DVN/ID1WNQ

Examples

>>> from eegdash.dataset import NM000165
>>> dataset = NM000165(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#