Reliable gradient search directions for kurtosis-based deflationary ICA: Application to physiological signal processing
Efficient gradient search directions for the optimisation of the kurtosis-based deflationary RobustICA algorithm in the case of real-valued data are proposed in this paper. The proposed scheme employs, in the gradient-like algorithm typically used to optimise the considered kurtosis-based objective function, search directions computed from a more reliable approximation of the negentropy than the kurtosis. The proposed scheme inherits the exact line search of the conventional RobustICA for which a good convergence property through a given direction is guaranteed. The efficiency of the proposed scheme is evaluated in terms of estimation quality, the execution time and the iterations count as a function of the number of used sensors and for different signal to noise ratios in the contexts of non-invasive epileptic ElectroEncephaloGraphic (EEG) and Magnetic Resonance Spectroscopic (MRS) analysis. The obtained results show that the proposed approach offer the best estimation performance/iterations count and execution time trade-off, especially in the case of high number of sensors.