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Towards optimizing electrode configurations for silent speech recognition based on high-density surface

Mingxing Zhu1,2,3, Haoshi Zhang1,2,3, Xiaochen Wang1,2

  • 1CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.

Journal of Neural Engineering
|November 12, 2020
PubMed
Summary
This summary is machine-generated.

Optimizing surface electromyography (sEMG) electrode placement significantly improves silent speech recognition (SSR) accuracy. Just ten strategically placed sEMG sensors achieve high recognition rates for spoken digits, outperforming numerous non-optimized sensors.

Keywords:
electrode placement optimizationhigh-density surface electromyographysequential forward selection algorithmsilent speech recognition

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Silent speech recognition (SSR) using surface electromyography (sEMG) offers a non-acoustic human-machine interface.
  • Electrode placement critically impacts SSR system performance due to complex neuromuscular activity in facial and neck muscles.
  • Previous SSR studies often used limited, empirically placed electrodes, leading to unreliable outcomes.

Purpose of the Study:

  • To systematically explore optimal electrode configurations for SSR using high-density sEMG.
  • To investigate the relationship between electrode number, placement, and SSR accuracy for digits 0-9.
  • To provide guidelines for clinically feasible SSR system development.

Main Methods:

  • Employed high-density sEMG with 120 closely spaced electrodes on facial and neck muscles.
  • Collected sEMG signals for silently spoken English and Chinese digits (0-9).
  • Utilized a sequential forward selection algorithm to identify optimal electrode subsets.

Main Results:

  • Classification accuracy rapidly increased with electrode number up to a saturation point.
  • Ten optimal electrodes achieved 86% accuracy for English and 94% for Chinese digits.
  • Optimal electrodes were predominantly on the neck; English recognition required more electrodes than Chinese for comparable accuracy.

Conclusions:

  • High-density sEMG and systematic electrode selection significantly enhance SSR performance.
  • Optimal electrode configurations, particularly on the neck, are crucial for effective SSR.
  • Findings offer practical guidelines for developing advanced human-machine interfaces for individuals with speech impairments.