Soft Computing

, Volume 20, Issue 4, pp 1289–1304 | Cite as

Decompositional independent component analysis using multi-objective optimization

  • Sim Kuan Goh
  • Hussein A. Abbass
  • Kay Chen TanEmail author
  • Abdullah Al-Mamun
Methodologies and Application


Current approaches for blind source separation, such as independent component analysis (ICA), implicitly assume that the number of collected signals equals the number of sources. This assumption does not hold true in many real-world applications as in the case of electroencephalographic (EEG) data collected from the surface of a human’s scalp, where independent EEG information is mixed with independent artifacts. This situation is abstracted in this paper by introducing the singers’ party problem, where the number of signals collected from the party equals the number of singers. However, there are also a number of instruments playing at the party representing independent sources that need to be removed correctly to extract the voices of the singers. In this paper, we introduce a decompositional approach to project the sources found in ICA into a higher-dimensional space; providing the ability to separate local (singers) information from shared/global (instruments) information. The decomposition will also associate each component with a mixed signal, creating a bijective relationship between the mixed signals and the sources. The problem is formulated as a multi-objective optimization problem. We compare the pros and cons of two different multi-objective formulations of the problem and demonstrate that one of the formulations can effectively solve the singers party problem.


Electroencephalography Independent component analysis  Multi-objective optimization Singers party problem 



The second author acknowledges funding from the University of New South Wales, Australia, that allowed him the time to conduct this work in Singapore. This work was also supported by the Singapore Ministry of Education Academic Research Fund Tier 1 under the project R-263-000-A12-112.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Sim Kuan Goh
    • 1
  • Hussein A. Abbass
    • 1
    • 2
  • Kay Chen Tan
    • 1
    Email author
  • Abdullah Al-Mamun
    • 1
  1. 1.Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore
  2. 2.School of Engineering and Information TechnologyUniversity of New South WalesCanberraAustralia

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