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Unsupervised neural decoding for concurrent and continuous multi-finger force prediction.

Long Meng1, Xiaogang Hu2

  • 1Department of Mechanical Engineering, Pennsylvania State University-University Park, PA, USA.

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|March 30, 2024
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Summary
This summary is machine-generated.

This study introduces an unsupervised neural decoding method for predicting multi-finger forces from spinal motoneuron firing, outperforming supervised methods. This advancement is key for developing better neural-machine interfaces for prosthetic control.

Keywords:
Biosignal processingFinger force predictionHand functionMachine learningUnsupervised neural decoding

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Rehabilitation Engineering
  • Machine Learning in Healthcare

Background:

  • Accurate prediction of multi-finger forces is essential for advanced neural-machine interfaces (NMIs).
  • Current supervised neural decoding methods require finger force data for training, limiting their applicability, especially for amputees.
  • Muscle co-activation complicates precise motor control decoding from surface electromyogram (sEMG) signals.

Purpose of the Study:

  • To develop and validate an unsupervised neural decoding approach for predicting multi-finger forces.
  • To utilize spinal motoneuron firing information derived from sEMG signals for decoding.
  • To improve the accuracy and robustness of NMIs, particularly for individuals with arm amputations.

Main Methods:

  • Extracted motor units (MUs) from high-density sEMG signals during isometric finger extensions.
  • Clustered MUs using dynamic time warping-based inter-MU distances to isolate relevant MUs.
  • Labeled MUs by firing rate and phase amplitude, then merged and weighted them for finger-specific force prediction.

Main Results:

  • The unsupervised approach achieved a higher R-squared value (0.77 ± 0.036) compared to supervised (0.71 ± 0.11) and conventional sEMG amplitude (0.61 ± 0.09) methods.
  • The proposed method demonstrated a lower root mean square error (5.16 ± 0.58 %MVC) than supervised (5.88 ± 1.34 %MVC) and sEMG amplitude (7.56 ± 1.60 %MVC) approaches.
  • Successfully teased out MUs from non-targeted fingers due to co-activation, enhancing prediction accuracy.

Conclusions:

  • The developed unsupervised neural decoding method provides a more accurate and robust alternative to existing approaches for predicting multi-finger forces.
  • This technique effectively handles muscle co-activation by clustering and labeling motor units.
  • Findings support the development of advanced NMIs for improved human-robotic hand interactions in various applications.