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A Spiking Neural Network in sEMG Feature Extraction.

Sergey Lobov1, Vasiliy Mironov2, Innokentiy Kastalskiy3

  • 1Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia. lobov@neuro.nnov.ru.

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|November 6, 2015
PubMed
Summary

A new hybrid neural network algorithm enhances surface electromyography (sEMG) feature extraction and classification accuracy. This novel approach shows robust performance across different data collection systems and enables effective mobile robot control.

Keywords:
artificial neural networkexoskeletonfeature extractionneurointerfacepattern classificationsEMG

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Machine Learning

Background:

  • Surface electromyography (sEMG) is crucial for human-computer interfaces.
  • Current sEMG classification methods face challenges in accuracy and adaptability.

Purpose of the Study:

  • To develop a novel algorithm for sEMG feature extraction and classification.
  • To evaluate the algorithm's performance and robustness across varying sEMG acquisition parameters.
  • To test the algorithm's efficacy in a practical application, such as mobile robot control.

Main Methods:

  • A hybrid neural network architecture combining spiking and artificial neurons was designed.
  • A spiking neuron layer with mutual inhibition was utilized for feature extraction.
  • The algorithm's classification accuracy was compared against existing sEMG interface systems.
  • The algorithm's sensitivity to different sEMG data sampling rates was assessed.

Main Results:

  • The proposed hybrid network achieved high classification accuracy, comparable to current sEMG systems.
  • The algorithm demonstrated consistent accuracy despite significant variations in sampling rates.
  • Successful application of the algorithm for controlling a mobile robot was achieved.

Conclusions:

  • The novel hybrid neural network offers a powerful and adaptable solution for sEMG analysis.
  • The algorithm's robustness to sampling rate differences makes it versatile for various sEMG systems.
  • This technology holds promise for advanced prosthetic control and human-robot interaction.