NDLI: Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals

Content Provider	IEEE Xplore Digital Library
Author	Farina, D. Fevotte, C. Doncarli, C. Merletti, R.
Copyright Year	1964
Abstract	Electromyographic (EMG) recordings detected over the skin may be mixtures of signals generated by different active muscles due to the phenomena related to volume conduction. Separation of the sources is necessary when single muscle activity has to be detected. Signals generated by different muscles may be considered uncorrelated but in general overlap in time and frequency. Under certain assumptions, mixtures of surface EMG signals can be considered as linear instantaneous but no a priori information about the mixing matrix is available when different muscles are active. In this study, we applied blind source separation (BSS) methods to separate the signals generated by two active muscles during a force-varying task. As the signals are non stationary, an algorithm based on spatial time-frequency distributions was applied on simulated and experimental EMG signals. The experimental signals were collected from the flexor carpi radialis and the pronator teres muscles which could be activated selectively for wrist flexion and rotation, respectively. From the simulations, correlation coefficients between the reference and reconstructed sources were higher than 0.85 for signals largely overlapping both in time and frequency and for signal-to-noise ratios as low as 5 dB. The Choi-Williams and Bessel kernels, in this case, performed better than the Wigner-Ville one. Moreover, the selection of time-frequency points for the procedure of joint diagonalization used in the BSS algorithm significantly influenced the results. For the experimental signals, the interference of the other source in each reconstructed source was significantly attenuated by the application of the BSS method. The ratio between root-mean-square values of the signals from the two sources detected over one of the muscles increased from (mean /spl plusmn/ standard deviation) 2.33/spl plusmn/1.04 to 4.51/spl plusmn/1.37 and from 1.55/spl plusmn/0.46 to 2.72/spl plusmn/0.65 for wrist flexion and rotation, respectively. This increment was statistically significant. It was concluded that the BSS approach applied is promising for the separation of surface EMG signals, with applications ranging from muscle assessment to detection of muscle activation intervals, and to the control of myoelectric prostheses.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	13
File Size	900133
Starting Page	1555
Ending Page	1567
File Format	PDF
ISSN	00189294
Volume Number	51
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Electromyography Source separation Signal generators Time frequency analysis Wrist Skin Signal detection Blind source separation Signal to noise ratio
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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