Blind source separation with wavelet optimization improved the cross-correlation coefficient to 0.92 and reduced experimental crosstalk to 15.2% compared to previous methods.
The proposed optimized wavelet approach for blind source separation of nonstationary surface EMG signals significantly improves source estimation and reduces crosstalk compared to previous methods.
Absolute Event Rate: 15.2% vs 35.1%
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.
Farina et al. (Thu,) conducted a other in Surface myoelectric signals (EMG). Blind source separation with wavelet optimization vs. Wigner-Ville distribution, Choi-Williams distribution, and second-order statistics was evaluated on Cross-correlation coefficient between original and estimated sources (simulated) and crosstalk reduction (experimental). Blind source separation with wavelet optimization improved the cross-correlation coefficient to 0.92 and reduced experimental crosstalk to 15.2% compared to previous methods.
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