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Updated: Mar 23, 2026

Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording
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Wearable optomyography enables continuous neuroprosthetic control.

Roman Khalikov1, Gurgen Soghoyan2, Mikhail Sintsov3

  • 1Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia. rom.khalikov@gmail.com.

Scientific Reports
|March 22, 2026
PubMed
Summary
This summary is machine-generated.

Optomyography (OMG) offers a novel, stable alternative to surface electromyography (sEMG) for wearable human-machine interfaces. This new wristband technology enables intuitive computer control with improved accuracy and versatility.

Keywords:
Brain-computer interfacesContinuous decodingGesture recognitionHuman-machine interfaceOptomyographyWearable devices

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Surface electromyography (sEMG) is used in wearable devices for human-machine interfaces but suffers from limitations like sensor instability and signal artifacts.
  • Existing sEMG techniques present challenges in sensor fixation, signal cross-talk, and long-term stability, hindering reliable computer command translation.

Purpose of the Study:

  • To introduce and evaluate optomyography (OMG) as a superior alternative to sEMG for wearable human-machine interfaces.
  • To demonstrate the efficacy of an OMG wristband system for computer control tasks.

Main Methods:

  • Developed a 50-channel optomyography (OMG) wristband system for capturing muscle activity.
  • Utilized a compact, fully connected neural network for decoding OMG signals based on hand gesture data.
  • Trained and tested the system with able-bodied participants and an individual with limb loss on computer mouse-like tasks.

Main Results:

  • Participants successfully controlled computer functions, including target acquisition and playing Tetris, using the OMG system.
  • Performance was evaluated using metrics like trajectory deviation and dwell time, showing improvements with training.
  • The OMG system demonstrated potential for enhanced performance, accuracy, stability, and versatility compared to traditional methods.

Conclusions:

  • Optomyography (OMG) presents a promising next-generation technology for wearable human-machine interfaces.
  • The developed OMG system offers a stable and versatile solution, overcoming limitations of conventional sEMG techniques.
  • OMG technology has the potential to significantly advance the capabilities of wearable devices for computer interaction.