TY - JOUR
T1 - A computationally affordable implementation of an asymptotically optimal BSS algorithm for AR sources
AU - Tichavský, Petr
AU - Doron, Eran
AU - Yeredor, Arie
AU - Nielsen, Jan
PY - 2006
Y1 - 2006
N2 - The second-order blind identification (SOBI) algorithm for separation of stationary sources was proved to be useful in many biomedical applications. This paper revisits the weights-adjusted variant of SOBI, known as WASOBI, which is asymptotically optimal (in separating Gaussian parametric processes), yet prohibitively computationally demanding for more than 2-3 sources. A computationally feasible implementation of the algorithm is proposed, which has a complexity not much higher than SOBI. Excluding the estimation of the correlation matrices, the post-processing complexity of SOBI is 0(d 4M), where d is the number of the signal components and M is the number of covariance matrices involved. The additional complexity of our proposed implementation of WASOBI is 0(d 6 + d 3M 3) operations. However, for WASOBI, the number M of the matrices can be significantly lower than that of SOBI without compromising performance. WASOBI is shown to significantly outperform SOBI in simulation, and can be applied, e.g., in the processing of low density EEG signals.
AB - The second-order blind identification (SOBI) algorithm for separation of stationary sources was proved to be useful in many biomedical applications. This paper revisits the weights-adjusted variant of SOBI, known as WASOBI, which is asymptotically optimal (in separating Gaussian parametric processes), yet prohibitively computationally demanding for more than 2-3 sources. A computationally feasible implementation of the algorithm is proposed, which has a complexity not much higher than SOBI. Excluding the estimation of the correlation matrices, the post-processing complexity of SOBI is 0(d 4M), where d is the number of the signal components and M is the number of covariance matrices involved. The additional complexity of our proposed implementation of WASOBI is 0(d 6 + d 3M 3) operations. However, for WASOBI, the number M of the matrices can be significantly lower than that of SOBI without compromising performance. WASOBI is shown to significantly outperform SOBI in simulation, and can be applied, e.g., in the processing of low density EEG signals.
UR - http://www.scopus.com/inward/record.url?scp=84862621096&partnerID=8YFLogxK
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AN - SCOPUS:84862621096
SN - 2219-5491
JO - European Signal Processing Conference
JF - European Signal Processing Conference
T2 - 14th European Signal Processing Conference, EUSIPCO 2006
Y2 - 4 September 2006 through 8 September 2006
ER -