NM000155: emg dataset, 6 subjects#
MUniverse Caillet et al 2023
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
6-participant EMG dataset — MUniverse Caillet et al 2023.
Quickstart#
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)
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'])
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#
BIDS-formatted version of the HDsEMG dataset published in *`Caillet et al. 2023 <https://doi.org/10.1523/ENEURO.0064-23.2023>`__*.
Six healthy male subjects (age: 26 +/- 4 years; height: 174 +/- 7 cm; weight: 66 +/- 15 kg).
Caillet et al 2023: HDsEMG recordings
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
View full README
Caillet et al 2023: HDsEMG recordings
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.
Cohort#
Dataset Statistics#
Sex composition
Channel counts: 259 ch (n=11 recordings)
Sampling frequencies: 2048.0 Hz (n=11 recordings)
Total recording duration: 7 min
Signal · Electrodes & live trace#
Live trace viewer — sub-01 · task-isometric30percentmvc · run-01
Showing one representative recording out of
6 subjects and 11 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 Caillet et al 2023 |
Author (year) |
|
Canonical |
— |
Importable as |
|
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 |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetNM000155 · Caillet2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.NM000155(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
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/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)
- 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 nm000155 to reproduce the tutorial on this dataset.
Citation
Arnault H. Caillet, Simon Avrillon, Aritra Kundu, Tianyi Yu, Andrew T. M. Phillips, … (20). MUniverse Caillet et al 2023. https://doi.org/10.7910/DVN/F9GWIW
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/F9GWIW.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset