Extraction of a source from multichannel data using sparse decomposition

It was discovered recently that sparse decomposition by signal dictionaries results in dramatic improvement of the qualities of blind source separation. We exploit sparse decomposition of a source in order to extract it from multidimensional sensor data, in applications where a rough template of the source is known. This leads to a convex optimization problem, which is solved by a Newton-type method. Complete and overcomplete dictionaries are considered. Simulations with synthetic evoked responses mixed into natural 122-channel MEG data show signi

• Type: Journal
• Year: 2002
• Where: IJON
• Authors: Michael Zibulevsky, Yehoshua Y. Zeevi
• Keywords: Blind Source Separation, IJON 2002, Multidimensional Sensor Data, Sparse Decomposition