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nm000108 NEMAR-native dataset

hyser_bids

HySER is a high-density surface electromyography (HD-sEMG) dataset comprising 256-channel recordings from 20 right-handed subjects performing five task types across two sessions. The dataset includes simultaneous 5-finger force measurements and covers discrete hand gesture recognition (34 gestures), maximum voluntary contractions, single-finger and multi-finger force tracking, and random contractions. This resource supports research in motor control, gesture recognition, and myoelectric signal processing.

EMG
Download zip Issues GitHub

Compute on this dataset

Two routes today, with a third (in-browser one-click submission) landing soon.

  1. NeuroScience Gateway (NSG) portal.

    NSG runs EEGLAB / Brainstorm / MNE pipelines on supercomputing time donated by SDSC. Create an account, point a job at this dataset's S3 prefix (s3://nemar/nm000108), and submit.
    nsgportal.org →

  2. Local processing with nemar-cli.

    Pull the dataset to your machine and run any toolbox locally. Honors the published version pinning. 

    npm install -g nemar-cli
nemar dataset clone nm000108
cd nm000108 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000108/ — the manifest carries presigned S3 URLs.

Direct compute access is coming soon. One-click NSG submission from this page is scoped for a follow-up phase. Tracked on nemarOrg/website#6.
DOI 10.82901/nemar.nm000108

![DOI](https://doi.org/10.82901/nemar.nm000108)

![Paper DOI](https://doi.org/10.1109/TNSRE.2021.3082551) ![PhysioNet](https://physionet.org/content/hd-semg/1.0.0/) ![License](https://opendatacommons.org/licenses/by/1-0/)

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 (signalelectrode 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.

Files

100 top-level entries · 108 GB total