MLP-BASED SOURCE SEPARATION FOR MLP-LIKE NONLINEAR MIXTURES

In this paper, the nonlinear blind source separation problem is addressed by using a multilayer perceptron (MLP) as separating system, which is justified in the universal approximation property of MLP networks. An adaptive learning algorithm for a perceptron with two hidden-layers is presented. The algorithm minimizes the mutual information between the outputs of the MLP. The performance of the proposed method is illustrated by some experiments. 1. INTRODUCTION. Blind Source Separation (BSS) is a fundamental problem in signal processing. It consists of retrieving unobserved sources s1(t),..., sN (t), assumed to be statistically independent (which is phisically plausible when the source

Brain connectivity analysis: a short survey

This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities

Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients

This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.This work has received research funding from the Spanish government (www.micinn.es) under project TEC2012 34306 (DiagnoSIS, Diagnosis by means of Statistical Intelligent Systems, 70K€) and projects P09-TIC-4530 (300K€) and P11-TIC-7103 (156K€) from the Andalusian government (http://www.juntadeandalucia.es/organismo​s/economiainnovacioncienciayempleo.html)

Competitive Learning, Simulated Annealing and Genetic Algorithms for the Separation of Sources

This paper presents a new adaptive procedure for the linear and non-linear separation of signals with nonuniform, symmetrical probability distributions, based on both simulated annealing (SA) and competitive learning (CL) methods by means of a neural network, considering the properties of the vectorial spaces of sources and mixtures, and using a multiple linearization in the mixture space. Also, the paper proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources in Nonlinear Mixtures (GABSS). Although the topic of BSS, by means of various techniques, including ICA, PCA, and neural networks, has been amply discussed in the literature, to date the possibility of using genetic algorithms has not been seriously explored. However, in Nonlinear Mixtures, optimization of the system parameters and, especially, the search for invertible functions is very difficult due to the existence of many local minima. From experimental results, this paper demonstrates the possible benefits offered by GAs in combination with BSS, such as robustness against local minima, the parallel search for various solutions, and a high degree of flexibility in the evaluation function.The main characteristics of the method are its simplicity and the rapid convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data

A Simple ICA Algorithm Based on Geometrical Approach

Here, a simple and new algorithm for blind separation of sources based on geometrical concepts is proposed. This algorithm deals with the instantaneous mixtures and it doesn't require the estimation of High Order Statistics (HOS)

World Multiconference on Systemics, Cybernetics and Informatics SCI 2001

In this paper we present a new blind separation of sources (BSS) algorithm based on second order statistics (SOS) and geometrical approaches. The new algorithm can separate multisources from their instantaneous mixtures obtained bymultisensors. In the case of p sources and p sensors, the algorithm can be decomposed into p steps: First, one should transform the mixing signals to orthogonal signals using mainly the SOS of the mixing signals. After that, one can separate the sources by using p 1 rotations and projections. The experimental studies show that the separation of two or three speechormusic signals can be obtained in relatively competitive time and that the obtained results are very satisfactory

A new approach to clustering and object detection with independent component analysis

It has previously been suggested that the visual cortex performs a data analysis similar to independent component analysis (ICA). Based on this idea we show that an incomplete ICA can be used to efficiently cluster independent components. We apply this algorithm to toy data, fMRI data and a set of natural scenes and show that this approach to clustering offers a wide variety of applications