Synchronized Multi-chain Mixture of Independent Component Analyzers

This paper presents a novel method for modeling the joint behavior of a number of synchronized Independent Component Analysis Mixture Models (ICAMM), which we have named Multi-chain ICAMM (MCICAMM). This allows flexible estimation of complex densities of data, subspace classification, blind source separation, accurate local dynamic learning, and global dynamic interaction. Furthermore, the proposed method can also be used for classification following the maximum a posteriori, forward-backward, or Viterbi procedures. MCICAMM outperformed competitive methods such as ICAMM, SICAMM, and Dynamic Bayesian Networks for the classification of simulated data and the automatic staging of electroencephalographic (EEG) data from epileptic patients performing a neuropsychological test for short-term memory. Therefore, the potential of the method to suit different kind of data densities and to deal with the changing non-stationarity and non-linearity of brain dynamics was demonstrated. MCICAMM parameters provide a structured result that might be interpreted in several applications.