Decompositional independent component analysis using multi-objective optimization
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.
KeywordsElectroencephalography 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.
- Abbass H (2014) Calibrating independent component analysis for real-time EEG artifacts removal. Lecture Notes Computer Science (LNCS) 8836:6875Google Scholar
- Abbass H, Tang J, Amin R, Ellejmi M, Kirby S (2014b) The computational air traffic control brain: computational red teaming and big data for real-time seamless brain-traffic integration. J Air Traffic Control 56(2):10–17Google Scholar
- Abbass H, Tang J, Amin R, Ellejmi M, Kirby S (2014) Augmented cognition using real-time EEG-based adaptive strategies for air traffic control. In: International Annual Meeting of the Human Factors and Ergonomic Society, HFES, SAGEGoogle Scholar
- Deb K et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. Evol Comput, IEEE Trans 6(2):182–197Google Scholar
- Delorme A, Plamer J, Oostenveld R, Onton J, Makeig S (2007) Comparing results of algorithms implementing blind source separation of EEG data. Swartz Foundation and NIH GrantGoogle Scholar
- Goh SK, Abbass HA, Tan KC (2014) Artifact removal from eeg using a multi-objective independent component analysis model. Lecture Notes Computer Science (LNCS) 8834:570577Google Scholar
- Vigário R, Jousmäki V, Hämäläninen M, Hari R, Oja E (1998) Independent component analysis for identification of artifacts in magnetoencephalographic recordings. Adv Neural Inf Process Syst 229–235Google Scholar