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Maximum Independent Component Analysis with Application to EEG Data

In many scientific disciplines, finding hidden influential factors 
behind observational data is essential but challenging. The majority of existing 
approaches, such as the independent component analysis (ICA), rely on 
linear transformation, that is, true signals are linear combinations of hidden 
components. Motivated from analyzing nonlinear temporal signals in neuroscience, 
genetics, and finance, this paper proposes the “maximum independent 
component analysis” (MaxICA), based on max-linear combinations of 
components. In contrast to existing methods, MaxICA benefits from focusing 
on significant major components while filtering out ignorable components. 
A major tool for parameter learning of MaxICA is an augmented genetic algorithm, 
consisting of three schemes for the elite weighted sum selection, 
randomly combined crossover, and dynamic mutation. Extensive empirical 
evaluations demonstrate the effectiveness of MaxICA in either extracting 
max-linearly combined essential sources in many applications or supplying a 
better approximation for nonlinearly combined source signals, such as EEG 
recordings analyzed in this paper.