Neural interfaces for hand control.

I build neural interfaces that turn muscle activity into control of virtual hands, orthoses and prostheses.

Raul C. Sîmpetru

Self-described content creator for journals.

Hands: LimbKit ↗
HD-EMG · 6422 DoF32 Hz

Featured work

Research areas.

01 · Decoding
From spinal motor neurons to a 22-degree-of-freedom hand model

Decoding hand movement from forearm EMG

We use forearm muscle recordings to estimate hand movement in real time, then investigate what the network learns.

02 · MyoGestic
A participant wearing the EMG bracelet controls a virtual hand, an orthosis, a prosthesis and a cursor

Decoding spared muscle activity for assistive control

We train decoders with participants to turn spared muscle activity into control of assistive devices.

03 · MyoGen
Cortical neurons, spinal motor neurons, afferents, muscle and the resulting EMG, simulated

Simulating neural drive, muscle and EMG

MyoGen links models of neural drive, muscle and EMG, letting us test decoders against known simulated signals.

Papers

Selected publications.

Decoding hand kinematics from EMG

  1. Camera setup, raw HD-sEMG, the 3D convolutional network and predicted joint angles

    14 jointspredicted from forearm EMG, 2.78° mean absolute error

    Paper ↗

    Accurate continuous prediction of 14 degrees of freedom of the hand from myoelectrical signals through convolutive deep learning. Raul C. Sîmpetru, Marius Osswald and 4 more. 44th IEEE EMBC · Glasgow, July 2022.

  2. Neural-to-EMG pathway and real-time hand-kinematics estimates compared with camera-based estimates

    >20 DoFdecoded live, under 50 ms latency, 32 predictions a second

    Paper ↗

    Proportional and simultaneous real-time control of the full human hand from high-density electromyography. Raul C. Sîmpetru, Michael März, Alessandro Del Vecchio. IEEE Trans. Neural Syst. Rehabil. Eng. 31 · July 2023.

Filtering and learned representations

  1. Neural information, movements and EMG, then filtering, a neural network and a comparison of filters

    320electrodes; best with all of them and no low-pass filtering

    Paper ↗Preprint ↗

    Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals. Raul C. Sîmpetru, Vlad Cnejevici and 2 more. J. Neural Eng. 21 · March 2024 · Open access.

  2. Neural pathway, the movement set, electrode grid placement and the 22-degree-of-freedom hand model

    22 DoFkinematics and force from 320 EMG sensors

    Article ↗Code ↗Paper ↗

    Learning a hand model from dynamic movements using high-density EMG and convolutional neural networks. Raul C. Sîmpetru, Andreas Arkudas and 6 more. IEEE Trans. Biomed. Eng. 71 · 2024.

Assistive control after injury or amputation

  1. Uninjured and motor-complete SCI participants, EMG and kinematics feed a deep learning model whose latent space separates gestures and groups

    98.3%mean accuracy across 8 attempted movements, 8 tetraplegic participants

    Paper ↗

    Identification of spared and proportionally controllable hand motor dimensions in motor complete spinal cord injuries using latent manifold analysis. Raul C. Sîmpetru, Daniela Souza de Oliveira and 2 more. IEEE Trans. Neural Syst. Rehabil. Eng. 32 · October 2024 · Open access.

  2. A participant wearing the EMG bracelet controls a virtual hand, an orthosis, a prosthesis and a cursor

    7 participantscontrol demonstrated with a virtual hand, orthosis, prosthesis and cursor

    Paper ↗Code ↗Archive ↗

    MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in individuals with neural lesions. Raul C. Sîmpetru†, Dominik I. Braun† and 11 more († equal contribution). Sci. Adv. 11, eads9150 · April 2025.

Biophysical simulation of neuromotor activity

  1. Cortical neurons, interneurons, motor neurons, afferents, a Hill-type muscle, volume conductor and resulting surface and intramuscular EMG

    Cortex to EMGsimulation of neural drive, muscle force, and surface and intramuscular EMG

    Preprint ↗Docs ↗Code ↗

    MyoGen: unified biophysical modeling of human neuromotor activity and resulting signals. Raul C. Sîmpetru*, Ricardo G. Molinari* and 5 more (* joint first authors). bioRxiv · January 2026.

Twelve more, as co-author

Software

Research software.

Real-time myocontrol software with decoder calibration during participant sessions.

Co-creator and core developer · v2.0, 2026

Docs ↗GitHub ↗Zenodo ↗

Biophysical simulation from neural drive to EMG.

Author and maintainer · v0.11.0 · pip install myogen

Docs ↗GitHub ↗

Experience

Research, teaching, talks.

The full CV (PDF) ↗

Raul C. Sîmpetru at a lectern at Georgian Technical University, presenting MyoGestic
Georgian Technical University, Tbilisi, 2026 ↗
Raul C. Sîmpetru at the lectern at ISEK 2026, presenting a slide titled The neural manifold
ISEK 2026, Jyväskylä, June 2026 ↗
Now
  • Doctoral researchern-squared lab, FAU Erlangen-Nürnberg
  • Invited professorKutaisi International University
Before
  • Research and teaching assistantFAU Erlangen-Nürnberg
  • Research assistantFraunhofer IIS
Education
  • Dr.-Ing. candidateDefence expected December 2026
  • MSc and BSc Medical EngineeringFAU Erlangen-Nürnberg
Teaching
  • Neurocomputing, robotics, deep learning, DSPKutaisi International University
  • Seminars and thesis supervisionFAU Erlangen-Nürnberg
Recognition
  • Best Student Paper, co-authorIEEE CBS 2026
  • Featured ArticleIEEE TBME 2024
  • Young Talent Award, 3rd prizeDMEA 2023
Talks
  • Public lecture: Muscles, Machines, and MovementGeorgian Technical University, Tbilisi 2026 ↗
  • Invited symposium talkISEK 2026
  • Rapid-fire talkInternational Motoneuron Society 2025
  • Oral talkISEK 2024
  • Selected oral talkThe Physiological Society 2023

Contact

raul.simpetru@fau.de