Statistical Signal Processing for Latent Variable

Statistical Signal processing may broadly be considered to involve the recovery of information from physical observations. Due to the random nature of the signal, statistical techniques play an important role in signal processing. Statistics is used in the formulation of appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters, and the assessment of model performances. This paper evaluates and compares the performance of Ways needed in fast ICA algorithm for decorrelation of the separating matrix can be deflationary or symmetric orthogonalization. Simulation studies reveal that symmetric approach has a better performance as compared to deflation approach, in terms of CPU time. The performance of the real-time applications such as speech signal enhancement and EEG/MEG essential features extraction for brain computer interface (BCI) based on MATLAB (R 2011a) process models.