NM000155: emg dataset, 6 subjects#

Name: MUniverse Caillet et al 2023
License: CC0 BY 4.0

MUniverse Caillet et al 2023

Access recordings and metadata through EEGDash.

Citation: Arnault H. Caillet, Simon Avrillon, Aritra Kundu, Tianyi Yu, Andrew T. M. Phillips, Luca Modenese, Dario Farina (20). MUniverse Caillet et al 2023. https://doi.org/10.7910/DVN/F9GWIW

Modality: emg Subjects: 6 Recordings: 11 License: CC0 BY 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000155

dataset = NM000155(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000155(cache_dir="./data", subject="01")

Advanced query

dataset = NM000155(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000155,
  title = {MUniverse Caillet et al 2023},
  author = {Arnault H. Caillet and Simon Avrillon and Aritra Kundu and Tianyi Yu and Andrew T. M. Phillips and Luca Modenese and Dario Farina},
  doi = {https://doi.org/10.7910/DVN/F9GWIW},
  url = {https://doi.org/https://doi.org/10.7910/DVN/F9GWIW},
}

About This Dataset#

Caillet et al 2023: HDsEMG recordings

BIDS-formatted version of the HDsEMG dataset published in *`Caillet et al. 2023 <https://doi.org/10.1523/ENEURO.0064-23.2023>`__*.

Population

Six healthy male subjects (age: 26 +/- 4 years; height: 174 +/- 7 cm; weight: 66 +/- 15 kg).

Protocol description

View full README

Caillet et al 2023: HDsEMG recordings

BIDS-formatted version of the HDsEMG dataset published in *`Caillet et al. 2023 <https://doi.org/10.1523/ENEURO.0064-23.2023>`__*.

Population

Six healthy male subjects (age: 26 +/- 4 years; height: 174 +/- 7 cm; weight: 66 +/- 15 kg).

Protocol description

Each participant performed two trapezoidal contractions at 30 percent and 50 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 and 15 s at 30 percent and 50 percent MVC, respectively. The order of the contractions was randomized.

Electrode placement

First, the skin was shaved, abrased and cleansed with 70 percent ethyl alcohol. Next, four grids (64 channels) were carefully positioned side-to-side with a 4-mm distance between the electrodes at the edges of adjacent grids. The 256 electrodes were centered on the muscle belly (right tibialis anterior) and laid within the muscle perimeter identified through palpation. Two bands damped with water were placed around the ankle as ground (R2) and reference (R1) electrodes.

Set-up description

The participant sat on a massage table with the hips flexed at 30 degrees, 0 degrees being the hip neutral position, and their knees fully extended. We fixed the foot of the dominant leg (right in all participants) onto the pedal of a commercial dynamometer (OT Bioelettronica) positioned at 30 degrees in the plantarflexion direction, 0 degrees being the foot perpendicular to the shank. The thigh was fixed to the massage table with an inextensible 3-cm-wide Velcro strap. The foot was fixed to the pedal with inextensible straps positioned around the proximal phalanx, metatarsal, and cuneiform. 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. The dynamometer was positioned according to the participant’s lower limb length and secured to the massage table to avoid any motion during the contractions.

Missing data

There is no 50 % MVC ramp-and-hold contraction for the second subject.

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 separate coordinate system (space-lowerLeg) reported as a percentage of the lower leg length.

Conversion

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

Dataset Information#

Dataset ID	`NM000155`
Title	MUniverse Caillet et al 2023
Author (year)	`Caillet2023`
Canonical	—
Importable as	`NM000155`, `Caillet2023`
Year	20
Authors	Arnault H. Caillet, Simon Avrillon, Aritra Kundu, Tianyi Yu, Andrew T. M. Phillips, Luca Modenese, Dario Farina
License	CC0 BY 4.0
Citation / DOI	https://doi.org/10.7910/DVN/F9GWIW
Source links	OpenNeuro \| NeMAR \| Source URL

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: 6
Recordings: 11
Tasks: 2

Channels & sampling rate

Channels: 259
Sampling rate (Hz): 2048.0
Duration (hours): 0.1227777777777777

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Sex distribution

6

Male Total: 6

Channel counts: 259 ch (n=11 recordings)

Sampling frequencies: 2048.0 Hz (n=11 recordings)

Total recording duration: 7 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000155 class to access this dataset programmatically.

class eegdash.dataset.NM000155(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

MUniverse Caillet et al 2023

Study:: nm000155 (NeMAR)
Author (year):: Caillet2023
Canonical:: —

Also importable as: NM000155, Caillet2023.

Modality: emg. Subjects: 6; recordings: 11; tasks: 2.

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/nm000155 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000155 DOI: https://doi.org/https://doi.org/10.7910/DVN/F9GWIW

Examples

>>> from eegdash.dataset import NM000155
>>> dataset = NM000155(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)