September 2011
Intermediate to advanced
324 pages
10h 9m
English
Content preview from Practical Biomedical Signal Analysis Using MATLAB
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216 Practical Biomedical Signal Analysis Using MATLAB
R
Blind source separation method was used by [Holobar et al., 2009] who mea-
sured sEMG from four muscles by means of 13x5 grid during low force contraction.
The approach was based on the convolution kernel compensation (CKC) algorithm
proposed by [Holobar and Zazula, 2007]. The signal was modeled as a convolutive
mixture of sparse pulse trains, which carry information about the rising times of the
detected symbols and the symbols themselves. The spatial and temporal statistics of
symbols, i.e., convolution kernels, was combined with the information about their
overlapping probability, in order to blindly reconstruct their pulse sequences. The
model implied admixture of a white noise, however residual ...
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