Andrzej Cichocki | RIKEN, Wako, Japan (original) (raw)

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Papers by Andrzej Cichocki

Research paper thumbnail of Tomographic image reconstruction from limited-view projections with Wiener filtered FOCUSS algorithm

Research paper thumbnail of Multichannel EEG Brain Activity Patterns Analysis in Time Frequency Domain with Nonnegative Matrix Factorization Support - Abstract

Research paper thumbnail of Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000

Research paper thumbnail of L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000

Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed ref... more Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine- cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.

Research paper thumbnail of Guest Editorial Special Issue on Information Theoretic Learning

IEEE Transactions on Neural Networks, 2000

Research paper thumbnail of A common neural-network model for unsupervised exploratory data analysis and independent component analysis

IEEE Transactions on Neural Networks, 1998

This paper presents the derivation of an unsupervised learning algorithm, which enables the ident... more This paper presents the derivation of an unsupervised learning algorithm, which enables the identification and visualization of latent structure within ensembles of high-dimensional data. This provides a linear projection of the data onto a lower dimensional subspace to identify the characteristic structure of the observations independent latent causes. The algorithm is shown to be a very promising tool for unsupervised exploratory data analysis and data visualization. Experimental results confirm the attractiveness of this technique for exploratory data analysis and an empirical comparison is made with the recently proposed generative topographic mapping (GTM) and standard principal component analysis (PCA). Based on standard probability density models a generic nonlinearity is developed which allows both 1) identification and visualization of dichotomised clusters inherent in the observed data and 2) separation of sources with arbitrary distributions from mixtures, whose dimensionality may be greater than that of number of sources. The resulting algorithm is therefore also a generalized neural approach to independent component analysis (ICA) and it is considered to be a promising method for analysis of real-world data that will consist of sub- and super-Gaussian components such as biomedical signals.

Research paper thumbnail of Table of contents

IEEE Transactions on Neural Networks, 2000

Research paper thumbnail of A robust approach to independent component analysis of signals with high-level noise measurements

IEEE Transactions on Neural Networks, 2003

We propose a robust approach for independent component analysis (ICA) of signals where observatio... more We propose a robust approach for independent component analysis (ICA) of signals where observations are contaminated with high-level additive noise and/or outliers. The source signals may contain mixtures of both sub-Gaussian and super-Gaussian components, and the number of sources is unknown. Our robust approach includes two procedures. In the first procedure, a robust prewhitening technique is used to reduce the power of additive noise, the dimensionality and the correlation among sources. A cross-validation technique is introduced to estimate the number of sources in this first procedure. In the second procedure, a nonlinear function is derived using the parameterized t-distribution density model. This nonlinear function is robust against the undue influence of outliers fundamentally. Moreover, the stability of the proposed algorithm and the robust property of misestimating the parameters (kurtosis) have been studied. By combining the t-distribution model with a family of light-tailed distributions (sub-Gaussian) model, we can separate the mixture of sub-Gaussian and super-Gaussian source components. Through the analysis of artificially synthesized data and real-world magnetoencephalographic (MEG) data, we illustrate the efficacy of this robust approach.

Research paper thumbnail of Self-Adaptive Blind Source Separation Based on Activation Functions Adaptation

IEEE Transactions on Neural Networks, 2004

Research paper thumbnail of Blind Estimation of Channel Parameters and Source Components for EEG Signals: A Sparse Factorization Approach

IEEE Transactions on Neural Networks, 2006

Research paper thumbnail of Blind Extraction of Global Signal From Multi-Channel Noisy Observations

IEEE Transactions on Neural Networks, 2000

We propose a novel efficient method of blind signal extraction from multi-sensor networks when ea... more 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.

Research paper thumbnail of Analysis and Online Realization of the CCA Approach for Blind Source Separation

IEEE Transactions on Neural Networks, 2000

A critical analysis of the canonical correlation analysis (CCA) approach in blind source separati... more A critical analysis of the canonical correlation analysis (CCA) approach in blind source separation (BSS) is provided. It is proved that by maximizing the autocorrelation functions of the recovered signals we can separate the source signals successfully. It is further shown that the CCA approach represents the same class of generalized eigenvalue decomposition (GEVD) problems as the matrix pencil method. Finally, online realizations of the CCA approach are discussed with a linear-predictor-based algorithm studied as an example.

Research paper thumbnail of An iterative inversion approach to blind source separation

IEEE Transactions on Neural Networks, 2000

Research paper thumbnail of Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering

IEEE Transactions on Neural Networks, 2000

Research paper thumbnail of From Blind Signal Extraction to Blind Instantaneous Signal Separation: Criteria, Algorithms, and Stability

IEEE Transactions on Neural Networks, 2004

Research paper thumbnail of Sparse Component Analysis and Blind Source Separation of Underdetermined Mixtures

IEEE Transactions on Neural Networks, 2005

Research paper thumbnail of Simplified neural networks for solving linear least squares and total least squares problems in real time

IEEE Transactions on Neural Networks, 1994

In this paper a new class of simplified low-cost analog artificial neural networks with on chip a... more In this paper a new class of simplified low-cost analog artificial neural networks with on chip adaptive learning algorithms are proposed for solving linear systems of algebraic equations in real time. The proposed learning algorithms for linear least squares (LS), total least squares (TLS) and data least squares (DLS) problems can be considered as modifications and extensions of well known algorithms: the row-action projection-Kaczmarz algorithm and/or the LMS (Adaline) Widrow-Hoff algorithms. The algorithms can be applied to any problem which can be formulated as a linear regression problem. The correctness and high performance of the proposed neural networks are illustrated by extensive computer simulation results.

Research paper thumbnail of Equivalence Probability and Sparsity of Two Sparse Solutions in Sparse Representation

IEEE Transactions on Neural Networks, 2000

This paper discusses the estimation and numerical calculation of the probability that the 0-norm ... more This paper discusses the estimation and numerical calculation of the probability that the 0-norm and 1-norm solutions of underdetermined linear equations are equivalent in the case of sparse representation. First, we define the sparsity degree of a signal. Two equivalence probability estimates are obtained when the entries of the 0-norm solution have different sparsity degrees. One is for the case in which the basis matrix is given or estimated, and the other is for the case in which the basis matrix is random. However, the computational burden to calculate these probabilities increases exponentially as the number of columns of the basis matrix increases. This computational complexity problem can be avoided through a sampling method. Next, we analyze the sparsity degree of mixtures and establish the relationship between the equivalence probability and the sparsity degree of the mixtures. This relationship can be used to analyze the performance of blind source separation (BSS). Furthermore, we extend the equivalence probability estimates to the small noise case. Finally, we illustrate how to use these theoretical results to guarantee a satisfactory performance in underdetermined BSS.

Research paper thumbnail of A switched-capacitor interface for capacitive sensors based on relaxation oscillators

IEEE Transactions on Instrumentation and Measurement, 1990

... to the unknown capacitance C, has been achieved to be di-rectly compatible with amicroprocess... more ... to the unknown capacitance C, has been achieved to be di-rectly compatible with amicroprocessor. ... and M. S. Beck, “A high frequency stray-immune capacitance transducer based on the ... and K. Watanabe, “A switched-capacitor charge-balancing analog-to-digital converter and ...

Research paper thumbnail of A novel switched-capacitor four-quadrant analog multiplier-divider and some of its applications

IEEE Transactions on Instrumentation and Measurement, 2000

Research paper thumbnail of Tomographic image reconstruction from limited-view projections with Wiener filtered FOCUSS algorithm

Research paper thumbnail of Multichannel EEG Brain Activity Patterns Analysis in Time Frequency Domain with Nonnegative Matrix Factorization Support - Abstract

Research paper thumbnail of Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000

Research paper thumbnail of L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000

Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed ref... more Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine- cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.

Research paper thumbnail of Guest Editorial Special Issue on Information Theoretic Learning

IEEE Transactions on Neural Networks, 2000

Research paper thumbnail of A common neural-network model for unsupervised exploratory data analysis and independent component analysis

IEEE Transactions on Neural Networks, 1998

This paper presents the derivation of an unsupervised learning algorithm, which enables the ident... more This paper presents the derivation of an unsupervised learning algorithm, which enables the identification and visualization of latent structure within ensembles of high-dimensional data. This provides a linear projection of the data onto a lower dimensional subspace to identify the characteristic structure of the observations independent latent causes. The algorithm is shown to be a very promising tool for unsupervised exploratory data analysis and data visualization. Experimental results confirm the attractiveness of this technique for exploratory data analysis and an empirical comparison is made with the recently proposed generative topographic mapping (GTM) and standard principal component analysis (PCA). Based on standard probability density models a generic nonlinearity is developed which allows both 1) identification and visualization of dichotomised clusters inherent in the observed data and 2) separation of sources with arbitrary distributions from mixtures, whose dimensionality may be greater than that of number of sources. The resulting algorithm is therefore also a generalized neural approach to independent component analysis (ICA) and it is considered to be a promising method for analysis of real-world data that will consist of sub- and super-Gaussian components such as biomedical signals.

Research paper thumbnail of Table of contents

IEEE Transactions on Neural Networks, 2000

Research paper thumbnail of A robust approach to independent component analysis of signals with high-level noise measurements

IEEE Transactions on Neural Networks, 2003

We propose a robust approach for independent component analysis (ICA) of signals where observatio... more We propose a robust approach for independent component analysis (ICA) of signals where observations are contaminated with high-level additive noise and/or outliers. The source signals may contain mixtures of both sub-Gaussian and super-Gaussian components, and the number of sources is unknown. Our robust approach includes two procedures. In the first procedure, a robust prewhitening technique is used to reduce the power of additive noise, the dimensionality and the correlation among sources. A cross-validation technique is introduced to estimate the number of sources in this first procedure. In the second procedure, a nonlinear function is derived using the parameterized t-distribution density model. This nonlinear function is robust against the undue influence of outliers fundamentally. Moreover, the stability of the proposed algorithm and the robust property of misestimating the parameters (kurtosis) have been studied. By combining the t-distribution model with a family of light-tailed distributions (sub-Gaussian) model, we can separate the mixture of sub-Gaussian and super-Gaussian source components. Through the analysis of artificially synthesized data and real-world magnetoencephalographic (MEG) data, we illustrate the efficacy of this robust approach.

Research paper thumbnail of Self-Adaptive Blind Source Separation Based on Activation Functions Adaptation

IEEE Transactions on Neural Networks, 2004

Research paper thumbnail of Blind Estimation of Channel Parameters and Source Components for EEG Signals: A Sparse Factorization Approach

IEEE Transactions on Neural Networks, 2006

Research paper thumbnail of Blind Extraction of Global Signal From Multi-Channel Noisy Observations

IEEE Transactions on Neural Networks, 2000

We propose a novel efficient method of blind signal extraction from multi-sensor networks when ea... more 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.

Research paper thumbnail of Analysis and Online Realization of the CCA Approach for Blind Source Separation

IEEE Transactions on Neural Networks, 2000

A critical analysis of the canonical correlation analysis (CCA) approach in blind source separati... more A critical analysis of the canonical correlation analysis (CCA) approach in blind source separation (BSS) is provided. It is proved that by maximizing the autocorrelation functions of the recovered signals we can separate the source signals successfully. It is further shown that the CCA approach represents the same class of generalized eigenvalue decomposition (GEVD) problems as the matrix pencil method. Finally, online realizations of the CCA approach are discussed with a linear-predictor-based algorithm studied as an example.

Research paper thumbnail of An iterative inversion approach to blind source separation

IEEE Transactions on Neural Networks, 2000

Research paper thumbnail of Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering

IEEE Transactions on Neural Networks, 2000

Research paper thumbnail of From Blind Signal Extraction to Blind Instantaneous Signal Separation: Criteria, Algorithms, and Stability

IEEE Transactions on Neural Networks, 2004

Research paper thumbnail of Sparse Component Analysis and Blind Source Separation of Underdetermined Mixtures

IEEE Transactions on Neural Networks, 2005

Research paper thumbnail of Simplified neural networks for solving linear least squares and total least squares problems in real time

IEEE Transactions on Neural Networks, 1994

In this paper a new class of simplified low-cost analog artificial neural networks with on chip a... more In this paper a new class of simplified low-cost analog artificial neural networks with on chip adaptive learning algorithms are proposed for solving linear systems of algebraic equations in real time. The proposed learning algorithms for linear least squares (LS), total least squares (TLS) and data least squares (DLS) problems can be considered as modifications and extensions of well known algorithms: the row-action projection-Kaczmarz algorithm and/or the LMS (Adaline) Widrow-Hoff algorithms. The algorithms can be applied to any problem which can be formulated as a linear regression problem. The correctness and high performance of the proposed neural networks are illustrated by extensive computer simulation results.

Research paper thumbnail of Equivalence Probability and Sparsity of Two Sparse Solutions in Sparse Representation

IEEE Transactions on Neural Networks, 2000

This paper discusses the estimation and numerical calculation of the probability that the 0-norm ... more This paper discusses the estimation and numerical calculation of the probability that the 0-norm and 1-norm solutions of underdetermined linear equations are equivalent in the case of sparse representation. First, we define the sparsity degree of a signal. Two equivalence probability estimates are obtained when the entries of the 0-norm solution have different sparsity degrees. One is for the case in which the basis matrix is given or estimated, and the other is for the case in which the basis matrix is random. However, the computational burden to calculate these probabilities increases exponentially as the number of columns of the basis matrix increases. This computational complexity problem can be avoided through a sampling method. Next, we analyze the sparsity degree of mixtures and establish the relationship between the equivalence probability and the sparsity degree of the mixtures. This relationship can be used to analyze the performance of blind source separation (BSS). Furthermore, we extend the equivalence probability estimates to the small noise case. Finally, we illustrate how to use these theoretical results to guarantee a satisfactory performance in underdetermined BSS.

Research paper thumbnail of A switched-capacitor interface for capacitive sensors based on relaxation oscillators

IEEE Transactions on Instrumentation and Measurement, 1990

... to the unknown capacitance C, has been achieved to be di-rectly compatible with amicroprocess... more ... to the unknown capacitance C, has been achieved to be di-rectly compatible with amicroprocessor. ... and M. S. Beck, “A high frequency stray-immune capacitance transducer based on the ... and K. Watanabe, “A switched-capacitor charge-balancing analog-to-digital converter and ...

Research paper thumbnail of A novel switched-capacitor four-quadrant analog multiplier-divider and some of its applications

IEEE Transactions on Instrumentation and Measurement, 2000

Research paper thumbnail of From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

In neurofeedback, brain waves are transformed into sounds or music, graphics, and other represent... more In neurofeedback, brain waves are transformed into sounds or music,
graphics, and other representations, to provide real-time information on ongoing
waves and patterns in the brain. Here we present various forms of neurofeedback,
including sonification, sonification in combination with visualization, and at last,
immersive neurofeedback, where auditory and visual feedback is provided in a
multi-sided immersive environment in which participants are completely surrounded by virtual imagery and 3D sound. Neural feedback may potentially
improve the user’s (or patient’s) ability to control brain activity, the diagnosis of
medical conditions, and the rehabilitation of neurological or psychiatric disorders.
Several psychological and medical studies have confirmed that virtual immersive
activity is enjoyable, stimulating, and can have a healing effect. As an illustration,
neurofeedback is generated from electroencephalograms (EEG) of Alzheimer’s
disease (AD) patients and healthy subjects. The auditory, visual, and immersive
representations of Alzheimer’s EEG differ substantially from healthy EEG, potentially
yielding novel diagnostic tools. Moreover, such alternative representations of
AD EEG are natural and intuitive, and hence easily accessible to laymen
(AD patients and family members)