18-12-2009, 04:04 PM
BLIND separation of independent sources has receiveda great deal of attention due to various applications inscience and technology. The problem of blind source separation(BSS) and/or ICA has been studied by many researchers inthe fields of neural networks and statistical signal processing[1]“[5], [9], [15], [16], [18], [22], [26], [31], [36] during thepast ten years, and many interesting theoretical and practicalresults have been achieved.This paper is organized as follows. A robust prewhitening technique with noise reduction and a cross-validation technique with optimal dimensionality reduction are presented in Section II. The parameterized t-distribution model and its robust properties, as well as the stability of the developed algorithm are presented in Section III. Experimental results using this new approach on artificially synthesized data and real-world unaveraged single-trial MEG data are presented in Section Invite MEG data are from an experiment studying the auditory evoked fields (AEF) task.
