Blind extraction of global signal from multi-channel noisy observations

Authors:
Yoshikazu Washizawa;Yukihiko Yamashita;Toshihisa Tanaka;Andrzej Cichocki
Affiliations:
Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama, Japan;Graduate School of Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan;Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan;Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama, Japan
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

We propose a novel efficient method of blind signal extraction from multi-sensor networks when each observed signal consists of one global signal and local uncorrelated signals. Most of existing blind signal separation and extraction methods such as independent component analysis have constraints such as statistical independence, non-Gaussianity, and underdetermination, and they are not suitable for global signal extraction problem from noisy observations. We developed an estimation algorithm based on alternating iteration and the smart weighted averaging. The proposed method does not have strong assumptions such as independence or non-Gaussianity. Experimental results using a musical signal and a real electroencephalogram demonstrate the advantage of the proposed method.