Blind Extraction of Global Signal From Multi-Channel Noisy Observations

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.

[1]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[2]  Jacek M. Leski.  Robust weighted averaging [of biomedical signals] , 2002, IEEE Transactions on Biomedical Engineering.

[3]  Andrzej Cichocki,et al.  Multichannel EEG brain activity pattern analysis in time–frequency domain with nonnegative matrix factorization support , 2007 .

[4]  M. S. Mobin,et al.  Weighted averaging of evoked potentials , 1992, IEEE Transactions on Biomedical Engineering.

[5]  J. Leski.  Robust weighted averaging. , 2002, IEEE transactions on bio-medical engineering.

[6]  Terrence J. Sejnowski,et al.  Blind source separation of more sources than mixtures using overcomplete representations , 1999, IEEE Signal Processing Letters.

[7]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[8]  Yaakov Tsaig,et al.  Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.

[9]  C. Herrmann.  Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.

[10]  Hagai Attias,et al.  Independent Factor Analysis , 1999, Neural Computation.

[11]  Olivier Meste,et al.  Weighted averaging using adaptive estimation of the weights , 1995, Signal Process..

[12]  A. Cichocki,et al.  Extraction of Steady State Visually Evoked Potential Signal and Estimation of Distribution Map from EEG Data , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[14]  Jian Li,et al.  ASEO: A Method for the Simultaneous Estimation of Single-Trial Event-Related Potentials and Ongoing Brain Activities , 2009, IEEE Transactions on Biomedical Engineering.

[15]  Van Khanh Nguyen,et al.  Blind Separation of Mutually Correlated Sources Using Precoders , 2010, IEEE Transactions on Neural Networks.

[16]  Daniel D. Lee,et al.  Multiplicative Updates for Nonnegative Quadratic Programming , 2007, Neural Computation.

[17]  P. Jaskowski,et al.  Amplitudes and latencies of single-trial ERP's estimated by a maximum-likelihood method , 1999, IEEE Transactions on Biomedical Engineering.

[18]  Schuster,et al.  Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.

[19]  B. Lütkenhöner,et al.  Possibilities and limitations of weighted averaging , 2004, Biological Cybernetics.

[20]  Hagai Attias,et al.  An Expectation–Maximization Method for Spatio–Temporal Blind Source Separation Using an AR-MOG Source Model , 2008, IEEE Transactions on Neural Networks.

[21]  A. Cichocki,et al.  Robust whitening procedure in blind source separation context , 2000 .

[22]  G Calhoun,et al.  Brain-computer interfaces based on the steady-state visual-evoked response. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[23]  Wei Liu,et al.  Blind source extraction of instantaneous noisy mixtures using a linear predictor , 2006, 2006 IEEE International Symposium on Circuits and Systems.