Farina, D.

Mesin, Luca

Tizzani, F.

English

PORTO Publications Open Repository TOrino

 1
0 2 4 6 8 10 12 14 16 18 20
0.5 au
Time (ms)
5 au
Estimated
Propagating
component
C
ha
nn
el
s
1
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Time (ms)
0 2 4 6 8 10 12 14 16 18 20
5 au
Estimated non
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Original signals
Reconstructed signals
EXAMPLE OF APPLICATION OF THE PROPOSED METHOD 
TO THE SEPARATION OF PROPAGATING AND NON-
PROPAGATING COMPONENTS FROM EXPERIMENTAL 
MONOPOLAR SINGLE MOTOR UNIT RECORDINGS 
(SIGNALS RECORDER FROM THE ABDUCTOR POLLICIS 
T02: EMG signal processing 
 
IDENTIFICATION OF NON-PROPAGATING COMPONENTS IN SURFACE EMG RECORDINGS BY 
OPTIMAL SPATIAL FILTERING  
Mesin L.(1), Tizzani F.(1), Farina D.(2) 
 
(1) LISiN, Dept. of Electronics, Politecnico di Torino, Torino, Italy. (2) Center for 
Sensory-Motor Interaction (SMI), Department of Health Science and Technology, 
Aalborg University, Aalborg, Denmark 
 
AIMS: A new method for the estimation of muscle fibre conduction (CV) from surface 
EMG signals is proposed, reducing the effect of non-propagating components. 
METHODS: Surface EMG signal is modelled as the sum of a propagating and a non-
propagating component. The Ppropagating component is assumed to travel without 
distortion across channels. The Nnon-propagating component is assumed to have the 
same shape but different amplitudes across channels. Given a pair of spatial filters 
obtained from 4 detection channels, CV was estimated by the optimal compensation 
of the spatial filter transfer functions with equivalent temporal filters (with transfer 
functions of the same shape as the spatial filters) dependent on the delay of 
propagation to be estimated. Only pair of spatial filters with vanishing weights were 
considered (resulting to two degrees of freedom). Each filter pair provides a different 
estimation of the delay, depending on how the non-propagating components are 
reduced by the spatial filters. The optimal filter pair (to be determined) annihilates 
the non-propagating components. Imposing this condition, a method to estimate 
propagating and non-propagating components was developed. The choice of the 
optimal filter pair was based on the accuracy in reconstructing the input signals by 
the sum of the estimated propagating and non-propagating components. 
RESULTS: The new method was applied to simulated and experimental EMG signals 
(Figure). Simulated signals were generated by a cylindrical, layered volume 
conductor model. Experimental signals 
were recorded from the abductor pollicis 
brevis with a linear array of 16 
electrodes. In the simulations, the 
proposed approach provided CV 
estimates with lower bias due to non-
propagating signal components than 
previously proposed methods (simulated 
value 4 m/s). Restricting CV values to 
the range 2-8 m/s, mean and standard 
deviations on a set of simulated signals 
(3 depths within the muscle, 3 fibre 
lengths, 10 realisation of 15 dB additive 
noise) were the following: new method 
3.92 ± 0.3 m/s (proposed method); 
spectral matching method 5.1 ± 1.2 m/s 
(spectral matching method); reference 
point (minimum) method 4.4 ± 0.9 m/s (reference points). In the experimental 
signals, the technique separated propagating and non-propagating signal 
components with an average reconstruction error of 2.9 ± 0.9% of the signal 
energy.  
CONCLUSIONS: A new method for the CV estimation of conduction velocity is proposed 
in this study. The technique is applicable to signals with one propagating and one non-
propagating component (single motor unit action potentials). The determination of the 
optimal filters also allows the separation of the propagating and non-propagating 
 2
signal components. The technique may find application in single motor unit studies for 
decreasing the variability and bias of CV estimates due to the presence and different 
weights of non-propagating components. 


Identification of Non-Propagating Components in Surface EMG Recordings by Optimal Spatial Filtering

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