Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement

McCool, Paul and Petropoulakis, Lykourgos and Soraghan, John and Chatlani, Navin (2015) Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement. Biomedical Signal Processing and Control, 18. pp. 61-68. ISSN 1746-8094 (https://doi.org/10.1016/j.bspc.2014.12.001)

[thumbnail of McCool-etal-BSPC-2014-Improved-pattern-recognition-classification-accuracy]
Preview
 PDF. Filename: McCool_etal_BSPC_2014_Improved_pattern_recognition_classification_accuracy.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (2MB)| Preview

Abstract

In this paper, we demonstrate that Spectral Enhancement techniques can be configured to improve the classification accuracy of a pattern recognition-based myoelectric control system. This is based on the observation that, when the subject is at rest, the power in EMG recordings drops to levels characteristic of the noise. Two Minimum Statistics techniques, which were developed for speech processing, are compared against electromyographic (EMG) de-noising methods such as wavelets and Empirical Mode Decomposition. In the cases of simulated EMG signals contaminated with white noise and for real EMG signals with added and intrinsic noise the gesture classification accuracy was shown to increase. The mean improvement in the classification accuracy is greatest when Improved Minima-Controlled Recursive Averaging (IMCRA)-based Spectral Enhancement is applied, thus demonstrating the potential of Spectral Enhancement techniques for improving the performance of pattern recognition-based myoelectric control.

CORE (COnnecting REpositories)