NM000108: emg dataset, 20 subjects#

Name: HySER: High-Density Surface Electromyogram Recordings
License: ODC-By-1.0

HySER: High-Density Surface Electromyogram Recordings

Access recordings and metadata through EEGDash.

Citation: Xinyu Jiang, Chenyun Dai, Xiangyu Liu, Jiahao Fan (20). HySER: High-Density Surface Electromyogram Recordings. 10.82901/nemar.nm000108

Modality: emg Subjects: 20 Recordings: 1514 License: ODC-By-1.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000108

dataset = NM000108(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 = NM000108(cache_dir="./data", subject="01")

Advanced query

dataset = NM000108(
    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{nm000108,
  title = {HySER: High-Density Surface Electromyogram Recordings},
  author = {Xinyu Jiang and Chenyun Dai and Xiangyu Liu and Jiahao Fan},
  doi = {10.82901/nemar.nm000108},
  url = {https://doi.org/10.82901/nemar.nm000108},
}

About This Dataset#

DOI Paper DOI PhysioNet License

HySER: High-Density Surface Electromyogram Recordings

BIDS-formatted version of the Hyser Surface EMG for Hand Gesture Recognition dataset (Jiang et al., 2021). 20 subjects performed 5 task types across 2 sessions using 256-channel high-density surface EMG (HD-sEMG) with simultaneous 5-finger force recordings.

Subjects

View full README

DOI Paper DOI PhysioNet License

HySER: High-Density Surface Electromyogram Recordings

BIDS-formatted version of the Hyser Surface EMG for Hand Gesture Recognition dataset (Jiang et al., 2021). 20 subjects performed 5 task types across 2 sessions using 256-channel high-density surface EMG (HD-sEMG) with simultaneous 5-finger force recordings.

Subjects

20 right-handed participants (12M, 8F; age 21-34). Two sessions per subject separated by 3-25 days. Demographics in participants.tsv.

Tasks

| Task | BIDS label | Description | Trials |
|------|-----------|-------------|--------|
| Pattern Recognition | `gesture01`-`gesture34` | 34 discrete hand gestures (Table I in paper) | 2 per gesture, each with 3 dynamic + 1 maintenance |
| Maximum Voluntary Contraction | `mvc` | MVC flexion/extension per finger | 2 per finger, 10s each |
| Single Finger (1-DOF) | `singlefinger` | Triangle force trajectory, individual fingers | 3 per finger, 25s each |
| Multi-Finger (N-DOF) | `multifinger` | Simultaneous multi-finger force tracking | 2 per combination, 25s each |
| Random | `random` | Free finger contractions at any force | 5 trials, 25s each |

Equipment

EMG system: Quattrocento (OT Bioelettronica, Torino, Italy), 2048 Hz, gain 150, 16-bit ADC
Electrodes: Four 8x8 gelled elliptical arrays (5mm x 2.8mm), 10mm inter-electrode distance
Placement: Two arrays on extensors (distal/proximal), two on flexors (distal/proximal) of right forearm
Reference: Olecranon (elbow); Ground: head of ulna (right leg drive)
Hardware filters: HP 10 Hz (2nd order), LP 500 Hz
Force sensors: SAS + HSGA (Huatran, Shenzhen, China), 100 Hz, 5 fingers

File Organization

*_emg.bdf - 256-channel EMG data (BDF format)
*_physio.tsv.gz - 5-finger force data (non-PR tasks only)
*_channels.tsv - Channel metadata with electrode mapping (signal_electrode column)
*_electrodes.tsv - Electrode positions in local grid coordinates (mm)
*_events.tsv - Event markers (gesture trials or segment boundaries)

Non-PR tasks (MVC, singlefinger, multifinger, random) are merged from multiple original recordings into single files per session, with boundary events marking segment junctions.

Coordinate Systems

Four local grid systems (space-ed, space-ep, space-fd, space-fp) in mm, anchored to a parent forearm system (space-forearm) in anatomical percent coordinates. See space-*_coordsystem.json files.

Missing Data

6 gesture recordings absent in source dataset (not conversion failures): sub-01/ses-2/gesture25, sub-03/ses-1/gesture04, sub-03/ses-2/gesture04, sub-05/ses-1/gesture34, sub-11/ses-1/gesture08, sub-19/ses-2/gesture11.

Conversion

Converted using EMG-2-BIDS (EEGLAB + bids-matlab-tools). Data integrity verified: mean Pearson correlation >0.9999 between original WFDB and converted BDF across all 1514 recordings. See dataset_description.json for generator details.

Dataset Information#

Dataset ID	`NM000108`
Title	HySER: High-Density Surface Electromyogram Recordings
Author (year)	`Jiang2021`
Canonical	—
Importable as	`NM000108`, `Jiang2021`
Year	20
Authors	Xinyu Jiang, Chenyun Dai, Xiangyu Liu, Jiahao Fan
License	ODC-By-1.0
Citation / DOI	10.82901/nemar.nm000108
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: 20
Recordings: 1514
Tasks: 38

Channels & sampling rate

Channels: 256
Sampling rate (Hz): Varies
Duration (hours): Not calculated

Electrode Layout#

Electrode layout — EMG · 256 sensors — 256 channels

Dataset Statistics#

Age distribution (n=20, range 21–34 yr)

202530

Sex distribution

8

12

Female Male Total: 20

Channel counts: 256 ch (n=1514 recordings)

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 NM000108 class to access this dataset programmatically.

class eegdash.dataset.NM000108(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

HySER: High-Density Surface Electromyogram Recordings

Study:: nm000108 (NeMAR)
Author (year):: Jiang2021
Canonical:: —

Also importable as: NM000108, Jiang2021.

Modality: emg. Subjects: 20; recordings: 1514; tasks: 38.

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/nm000108 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000108 DOI: https://doi.org/10.82901/nemar.nm000108

Examples

>>> from eegdash.dataset import NM000108
>>> dataset = NM000108(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)