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24
Blind Source Separation and Independent Component Analysis: A Review
, 2004
"... Blind source separation (BSS) and independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. A recent trend in BSS is to consider problems in the framework of matr ..."
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Blind source separation (BSS) and independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. A recent trend in BSS is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree structured graphical models and exploit a priori knowledge about true nature and structure of latent (hidden) variables or sources such as spatiotemporal decorrelation, statistical independence, sparseness, smoothness or lowest complexity in the sense e.g., of best predictability. The possible goal of such decomposition can be considered as the estimation of sources not necessary statistically independent and parameters of a mixing system or more generally as finding a new reduced or hierarchical and structured representation for the observed (sensor) data that can be interpreted as physically meaningful coding or blind source estimation. The key issue is to find a such transformation or coding (linear or nonlinear) which has true physical meaning and interpretation. We present a review of BSS and ICA, including various algorithms for static and dynamic models and their applications. The paper mainly consists of three parts:
Generalized component analysis and blind source separation methods for analyzing multichannel brain signals
 Statistical and Process Models of Cognitive Aging, Mahwah, NJ
, 2006
"... Blind source separation (BSS) and related methods, e.g., independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. The recent trends in blind source separation and ..."
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Blind source separation (BSS) and related methods, e.g., independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. The recent trends in blind source separation and generalized component analysis (GCA) is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative models and exploit a priori knowledge about true nature, morphology or structure of latent (hidden) variables or sources such as sparseness, spatiotemporal decorrelation, statistical independence, nonnegativity, smoothness or lowest possible complexity. The goal of BSS can be considered as estimation of true physical sources and parameters of a mixing system, while objective of GCA is finding a new reduced or hierarchical and structured representation for the observed (sensor) data that can be interpreted as physically meaningful coding or blind signal decompositions. The key issue is to find a such transformation or coding which has true physical meaning and interpretation. In this paper we discuss some promising applications of BSS/GCA for analyzing multimodal, multisensory data, especially EEG/MEG data. Moreover, we propose to apply
Ensemble neural network approach for accurate load forecasting in M. Sikora and B. Sikora a power system
 International Journal of Applied Mathematics and Computer Science 19(2): 303–315, DOI
, 2009
"... The paper presents an improved method for 1–24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perce ..."
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The paper presents an improved method for 1–24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on selforganizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.
Blind Signal Processing Methods for Analyzing
"... Abstract. A great challenge in neurophysiology is to asses noninvasively the physiological changes occurring in different parts of the brain. These activation can be modeled and measured often as neuronal brain source signals that indicate the function or malfunction of various physiological subsys ..."
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Abstract. A great challenge in neurophysiology is to asses noninvasively the physiological changes occurring in different parts of the brain. These activation can be modeled and measured often as neuronal brain source signals that indicate the function or malfunction of various physiological subsystems. To extract the relevant information for diagnosis and therapy, expert knowledge is required not only in medicine and neuroscience but also statistical signal processing. Besides classical signal analysis tools (such as adaptive supervised filtering, parametric or nonparametric spectral estimation, timefrequency analysis, and higherorder statistics), new and emerging blind signal processing (BSP) methods, especially, generalized component analysis (GCA) including fusion (integration) of independent component analysis (ICA), sparse component analysis (SCA), timefrequency component analyzer (TFCA) and nonnegative matrix factorization (NMF) can be used for analyzing brain data, especially for noise reduction and artefacts elimination, enhancement, detection and estimation of neuronal brain source signals. The recent trends in the BSP is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree structured graphical models and exploit a priori knowledge about true nature and structure of latent (hidden) variables or brain sources such as spatiotemporal decorrelation, statistical independence,
OCULAR ARTIFACTS REMOVAL IN SCALP EEG: COMBINING ICA AND WAVELET DENOISING
"... In this communication we present the first results of a project whose goal is to remove artifacts from electroencephalographic epileptic signals. More precisely the present objective is to remove ocular (blinking) artifacts in simulated and real EEG signal using Independent Component Analysis and wa ..."
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In this communication we present the first results of a project whose goal is to remove artifacts from electroencephalographic epileptic signals. More precisely the present objective is to remove ocular (blinking) artifacts in simulated and real EEG signal using Independent Component Analysis and wavelet denoising algorithms. Epilepsy is one of the most common brain disorders. It is characterized by repeated seizures, which range from the shortest lapse in attention to severe, frequent convulsions. They can occur from several times a day to once every few
Mimo instantaneous blind identification based on secondorder temporal structure,” Signal Processing
 IEEE Transactions on
, 2008
"... Abstract—Blind identification is a crucial subtask in signal processing problems such as blind signal separation (BSS) and directionofarrival (DOA) estimation. This paper presents a procedure for multipleinput multipleoutput instantaneous blind identification based on secondorder temporal prope ..."
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Abstract—Blind identification is a crucial subtask in signal processing problems such as blind signal separation (BSS) and directionofarrival (DOA) estimation. This paper presents a procedure for multipleinput multipleoutput instantaneous blind identification based on secondorder temporal properties of the signals, such as coloredness and nonstationarity. The procedure consists of two stages. First, based on assumptions on the secondorder temporal structure (SOTS) of the source and noise signals, and using subspace techniques, the problem is reformulated in a particular way such that each column of the unknown mixing matrix satisfies a system of multivariate homogeneous polynomial equations. Then, this nonlinear system of equations is solved by means of a socalled homotopy method, which provides a general tool for solving (possibly nonexact) systems of nonlinear equations by smoothly deforming the known solutions of a simple start system into the desired solutions of the target system. Our blind identification procedure allows to estimate the mixing matrix for scenarios with more sources than sensors without resorting to sparsity assumptions, something that is often believed to be impossible when using only secondorder statistics. In addition, since our algorithm does not require any assumption on the mixing matrix, also mixing matrices that are rankdeficient or even have identical columns can be identified. Finally, we give examples and performance results for speech source signals. Index Terms—Blind identification/separation, homogeneous system, homotopy, secondorder statistics, temporal structure.
Maximum likelihood blind image separation using nonsymmetrical halfplane markov random fields
 IEEE Trans. on Image Processing
, 2009
"... Abstract—This paper presents a maximum likelihood approach for blindly separating linear instantaneous mixtures of images. The spatial autocorrelation within each image is described using nonsymmetrical halfplane (NSHP) Markov random fields in order to simplify the joint probability density functi ..."
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Abstract—This paper presents a maximum likelihood approach for blindly separating linear instantaneous mixtures of images. The spatial autocorrelation within each image is described using nonsymmetrical halfplane (NSHP) Markov random fields in order to simplify the joint probability density functions of the source images. A first implementation assuming stationary sources is presented. It is then extended to a more realistic nonstationary image model: two approaches, respectively based on blocking and kernel smoothing, are proposed to cope with the nonstationarity of the images. The estimation of the mixing matrix is performed using an iterative equivariant version of the NewtonRaphson algorithm. Moreover, score functions, required for the computation of the updating rule, are approximated at each iteration by parametric polynomial estimators. Results achieved with artificial mixtures of both artificial and realworld images, including an astrophysical application, clearly prove the high performance of our methods, as compared to classical algorithms. Index Terms—Blind source separation (BSS), maximum likelihood approach, nonstationary sources, nonsymmetrical halfplane (NSHP) Markov random fields. I.
A Computational Framework to Support the Automated Analysis of Routine Electroencephalographic Data
, 2010
"... Epilepsy is a condition in which a patient has multiple unprovoked seizures which are not precipitated by another medical condition. It is a common neurological disorder that afflicts 1% of the population of the US, and is sometimes hard to diagnose if seizures are infrequent. Routine Electroencepha ..."
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Epilepsy is a condition in which a patient has multiple unprovoked seizures which are not precipitated by another medical condition. It is a common neurological disorder that afflicts 1% of the population of the US, and is sometimes hard to diagnose if seizures are infrequent. Routine Electroencephalography (rEEG), where the electrical potentials of the brain are recorded on the scalp of a patient, is one of the main tools for diagnosing because rEEG can reveal indicators of epilepsy when patients are in a nonseizure state. Interpretation of rEEG is difficult and studies have shown that 2030 % of patients at specialized epilepsy centers are misdiagnosed [18, 73]. An improved ability to interpret rEEG could decrease the misdiagnosis rate of epilepsy. The difficulty in diagnosing epilepsy from rEEG stems from the large quantity, low signal to noise ratio (SNR), and variability of the data. A usual point of error for a clinician interpreting rEEG data is the misinterpretation of PEEs (paroxysmal EEG events) – short bursts of electrical
Blind Separation of Piecewise Stationary NonGaussian Sources
, 2009
"... We address Independent Component Analysis (ICA) of piecewise stationary and nonGaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The method is a further extension of the popular nonGaussianitybased FastICA algorithm and of its ..."
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We address Independent Component Analysis (ICA) of piecewise stationary and nonGaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The method is a further extension of the popular nonGaussianitybased FastICA algorithm and of its recently optimized variant called EFICA. In contrast to these methods, Block EFICA is developed to effectively exploit varying distribution of signals, thus, also their varying variance in time (nonstationarity) or, more precisely, in timeintervals (piecewise stationarity). In theory, the accuracy of the method asymptotically approaches CramérRao lower bound (CRLB) under common assumptions when variance of the signals is constant. On the other hand, the performance is practically close to the CLRB even when variance of the signals is changing. This is demonstrated by comparing our algorithm with various methods that are asymptotically efficient within ICA models based either on the nonGaussianity or the nonstationarity. The benefit of our algorithm is demonstrated by examples with realworld audio signals.
FSEONS: A SecondOrder FrequencyDomain Algorithm for Noisy Convolutive Source Separation
"... Abstract — We present a frequencydomain method of noisy convolutive source separation, where we extends the SEONS algorithm [2] that jointly exploits the nonstationarity and temporal structure of sources. Thus, the method is called FSEONS, implying Frequencydomain SEONS. Unlike most of existing m ..."
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Abstract — We present a frequencydomain method of noisy convolutive source separation, where we extends the SEONS algorithm [2] that jointly exploits the nonstationarity and temporal structure of sources. Thus, the method is called FSEONS, implying Frequencydomain SEONS. Unlike most of existing methods of convolutive source separation, we consider the case of noisy data and show that our FSEONS algorithm identify multivariate FIR channels in a robust way. In addition, we employ an H ∞ filtering method in order to further suppress the sensor noise. Numerical experiments with comparing to other methods, confirm the high performance of our proposed method. I.