Nonlinear ICA Based on a Combined Neural Network and Its Application to Single-Trial Extraction of SCP

A combined, unsupervised, multilayer perceptron neural network model based on MISEP and NLFA is presented to resolve the separation problem of nonlinear ICA, the separation performances of signals are compared between two sigmoid functions used in the latent layers of MISEP and introduced RBF. Experimental results show this algorithm can recover sources from nonlinear mixtures better and has good stabilization, it is also applied to single-trial extraction of SCP, the whole effect is evident compared with the averaged method.