EEGdash›NeMAR›NM000165

Iss. 165 · 1 subjects · 10 recordings · CC0 BY 4.0

Dataset Brief · MUniverse Grison et al 2025

NM000165: emg dataset, 1 subjects#

Name: MUniverse Grison et al 2025
License: CC0 BY 4.0

MUniverse Grison et al 2025

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

1-participant EMG dataset — MUniverse Grison et al 2025.

Data & curation Agnese Grison · Irene Mendez Guerra · Alexander Kenneth Clarke · Silvia Muceli · Jaime Ibanez Pereda · Dario Farina
Year 20 · Distributed via NeMAR
Funding UK Research and Innovation (UKRI Centre for Doctoral Training in AI for Healthcare grant number EP/S023283/1) · Huawei Technologies Research & Development (UK) Ltd. · + 6 more

EMG · 131 ch10240 HzBIDS 1.11.110 tasks

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

§ 02Study · The README

About This Dataset#

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

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.

Grison et al 2025: HDsEMG recordings

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.

View full README

Grison et al 2025: HDsEMG recordings

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.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Sex composition

Male
1

HandednessRight · 1

Channel counts: 131 ch (n=10 recordings)

Sampling frequencies: 10240.0 Hz (n=10 recordings)

Total recording duration: 8 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 131 ch · EMG · 10240 Hz · 1 subjects, 10 recordings

Live trace viewer — sub-01 · task-isometric35percentmvc · run-01

Showing one representative recording out of 1 subjects and 10 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000165(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Grison2025

Canonical—

Importable asNM000165 · Grison2025

Sourceeegdash/dataset/registry.py · [source ↗]

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

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: 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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000165.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 nm000165 to reproduce the tutorial on this dataset.

Citation

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

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/ID1WNQ.

Related & sibling datasets

NM000108EMG · 20 subj NM000155EMG · 6 subj NM000159EMG · 16 subj NM000105EMG · 100 subj NM000107EMG · 100 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.11.1

Sidecars

channels · electrodes

Provenance

CC0 BY 4.0 · https://doi.org/10.7910/DVN/ID1WNQ

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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

NM000165: emg dataset, 1 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#