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Optimized wavelets for blind separation of nonstationary surface myoelectric signals. | LitMetric

Optimized wavelets for blind separation of nonstationary surface myoelectric signals.

IEEE Trans Biomed Eng

Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.

Published: January 2008

AI Article Synopsis

  • Surface electromyography (EMG) signals often mix inputs from multiple muscles, making it difficult to isolate individual muscle activity due to poor spatial selectivity in recordings.
  • This paper presents a novel method for blind source separation (BSS) of these signals, utilizing whitening and joint diagonalization of spatial wavelet distributions (SWDs) to enhance signal clarity.
  • Testing showed that this method significantly reduced crosstalk between muscle signals, resulting in more accurate estimations when compared to other statistical approaches and traditional wavelets.

Article Abstract

Surface electromyography (EMG) signals detected over the skin surface may be mixtures of signals generated by many active muscles due to poor spatial selectivity of the recording. In this paper, we propose a new method for blind source separation (BSS) of nonstationary signals modeled as linear instantaneous mixtures. The method is based on whitening of the observations and rotation of the whitened observations. The rotation is performed by joint diagonalization of a set of spatial wavelet distributions (SWDs). The SWDs depend on the selection of the mother wavelet which can be defined by unconstrained parameters via the lattice parameterization within the multiresolution analysis framework. As the sources are classically supposed to be mutually uncorrelated, the design parameters of the mother wavelet can be blindly optimized by minimizing the average (over time lags) cross correlation between the estimated sources. The method was tested on simulated and experimental surface EMG signals and results were compared with those obtained with spatial time-frequency distributions and with second-order statistics (only spectral information). On a set of simulated signals, for 10-dB signal-to-noise ratio (SNR), the cross-correlation coefficient between original and estimated sources was 0.92 +/- 0.07 with wavelet optimization, 0.74 +/- 0.09 with the wavelet leading to the poorest performance, 0.85 +/- 0.07 with Wigner-Ville distribution, 0.86 +/- 0.07 with Choi-Williams distribution, and 0.73 +/- 0.05 with second-order statistics. In experimental conditions, when the flexor carpi radialis and pronator teres were concomitantly active for 50% of the time, crosstalk was 55.2 +/- 10.0% before BSS and was reduced to 15.2 +/- 6.3% with wavelet optimization, 30.1 +/- 15.0% with the worst wavelet, 28.3 +/- 12.3% with Wigner-Ville distribution, 26.2 +/- 12.0% with Choi-Williams distribution, and 35.1 +/- 15.5% with second-order statistics. In conclusion, the proposed approach resulted in better performance than previous methods for the separation of nonstationary myoelectric signals.

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Source
http://dx.doi.org/10.1109/TBME.2007.897844DOI Listing

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