LAUSR

Blind source separation (BSS), i.e. extracting unknown sources from mixtures of them, has attracted great interest in various fields of signal processing, for instance in neurophysiological data analysis. By increasing the availability of multiple and complementary data, associated with a given case, many joint BSS (JBSS) algorithms have been developed, which attempt to jointly analyze all datasets and meaningfully integrate information from them. In this letter, we study the special case of multiple datasets analysis, where all datasets share the same set of underlying sources which contribute to different datasets through different mixing matrices. With the aim of efficient dimension reduction, we propose a novel joint preprocessing method to accurate and robust source separation from multipldatasets. We sequentially separate the source with the most significant impact on all datasets on average. To this end, the magnitudes of source projections on the subspaces spanned by each dataset are obtained and sum of their $p{th}$ power is maximized. Dependency between datasets can be efficiently treated using $p$. Simulation results show the considerable improvement of the proposed method over existing JBSS algorithms.