Results 1  10
of
80
Blind Separation of Instantaneous Mixtures of Non Stationary Sources
 IEEE Trans. Signal Processing
, 2000
"... Most ICA algorithms are based on a model of stationary sources. This paper considers exploiting the (possible) nonstationarity of the sources to achieve separation. We introduce two objective functions based on the likelihood and on mutual information in a simple Gaussian non stationary model and w ..."
Abstract

Cited by 167 (12 self)
 Add to MetaCart
(Show Context)
Most ICA algorithms are based on a model of stationary sources. This paper considers exploiting the (possible) nonstationarity of the sources to achieve separation. We introduce two objective functions based on the likelihood and on mutual information in a simple Gaussian non stationary model and we show how they can be optimized, offline or online, by simple yet remarkably efficient algorithms (one is based on a novel joint diagonalization procedure, the other on a Newtonlike technique). The paper also includes (limited) numerical experiments and a discussion contrasting nonGaussian and nonstationary models. 1. INTRODUCTION The aim of this paper is to develop a blind source separation procedure adapted to source signals with time varying intensity (such as speech signals). For simplicity, we shall restrict ourselves to the simplest mixture model: X(t) = AS(t) (1) where X(t) = [X 1 (t) XK (t)] T is the vector of observations (at time t), A is a fixed unknown K K inver...
Boosting bit rates in noninvasive EEG singletrial classifications by feature combination and multiclass paradigms
, 2004
"... Noninvasive EEG recordings provide for easy and safe access to human neocortical processes which can be exploited for a BrainComputer Interface (BCI). At present, however, the use of BCIs is severely limited by low bittransfer rates. Here, we systematically analyze and furthermore develop two rec ..."
Abstract

Cited by 96 (19 self)
 Add to MetaCart
Noninvasive EEG recordings provide for easy and safe access to human neocortical processes which can be exploited for a BrainComputer Interface (BCI). At present, however, the use of BCIs is severely limited by low bittransfer rates. Here, we systematically analyze and furthermore develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: (1) the combination of classifiers each specifically tailored for different physiological phenomena, e.g. slow cortical potential shifts, such as the premovement Bereitschaftspotential, or differences in spatiospectral distributions of brain activity (i.e. focal eventrelated desynchronizations), and (2) behavioral paradigms inducing the subjects to generate one out of several brain states (multi class approach) which all bare a distinctive spatiotemporal signature well discriminable in the standard scalp EEG. We derive informationtheoretic predictions and demonstrate their relevance in experimental data. We will show in particular that a suitably arranged interaction between these concepts can significantly boost BCI performances.
A Fast Algorithm for Joint Diagonalization with Nonorthogonal Transformations and its Application to Blind Source Separation
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2004
"... A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm is based on the Frobeniusnorm formulation of the joint diagonalization problem, and addresses diagonalization with a general, nonorthogonal transformation. The iterative scheme of the algorithm i ..."
Abstract

Cited by 45 (5 self)
 Add to MetaCart
A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm is based on the Frobeniusnorm formulation of the joint diagonalization problem, and addresses diagonalization with a general, nonorthogonal transformation. The iterative scheme of the algorithm is based on a multiplicative update which ensures the invertibility of the diagonalizer. The algorithm 's efficiency stems from the special approximation of the cost function resulting in a sparse, blockdiagonal Hessian to be used in the computation of the quasiNewton update step. Extensive numerical simulations illustrate the performance of the algorithm and provide a comparison to other leading diagonalization methods. The results of such comparison demonstrate that the proposed algorithm is a viable alternative to existing stateoftheart joint diagonalization algorithms.
Dimensionality reduction in higherorder signal processing and rank(R_1,R__2,...,R_N) reduction in multilinear algebra
, 2004
"... ..."
Increase information transfer rates in BCI by CSP extension to multiclass
, 2004
"... BrainComputer Interfaces (BCI) are an interesting emerging technology that is driven by the motivation to develop an effective communication interface translating human intentions into a control signal for devices like computers or neuroprostheses. If this can be done bypassing the usual human ..."
Abstract

Cited by 25 (8 self)
 Add to MetaCart
(Show Context)
BrainComputer Interfaces (BCI) are an interesting emerging technology that is driven by the motivation to develop an effective communication interface translating human intentions into a control signal for devices like computers or neuroprostheses. If this can be done bypassing the usual human output pathways like peripheral nerves and muscles it can ultimately become a valuable tool for paralyzed patients. Most activity in BCI research is devoted to finding suitable features and algorithms to increase information transfer rates (ITRs). The present paper studies the implications of using more classes, e.g., left vs. right hand vs. foot, for operating a BCI. We contribute by (1) a theoretical study showing under some mild assumptions that it is practically not useful to employ more than three or four classes, (2) two extensions of the common spatial pattern (CSP) algorithm, one interestingly based on simultaneous diagonalization, and (3) controlled EEG experiments that underline our theoretical findings and show excellent improved ITRs.
Second order nonstationary source separation
 in Journal of VLSI Signal Processing
"... Abstract. This paper addresses a method of blind source separation that jointly exploits the nonstationarity and temporal structure of sources. The method needs only multiple timedelayed correlation matrices of the observation data, each of which is evaluated at different timewindowed data frame, ..."
Abstract

Cited by 24 (4 self)
 Add to MetaCart
Abstract. This paper addresses a method of blind source separation that jointly exploits the nonstationarity and temporal structure of sources. The method needs only multiple timedelayed correlation matrices of the observation data, each of which is evaluated at different timewindowed data frame, to estimate the demixing matrix. The method is insensitive to the temporally white noise since it is based on only timedelayed correlation matrices (with nonzero timelags) and is applicable to the case of either nonstationary sources or temporally correlated sources. We also discuss the extension of some existing methods with the overview of secondorder blind source separation methods. Extensive numerical experiments confirm the validity and high performance of the proposed method.
Blind Separation of Postnonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... We propose two methods that reduce the postnonlinear blind source separation problem (PNLBSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithma powerful technique from nonparametric stati ..."
Abstract

Cited by 24 (2 self)
 Add to MetaCart
We propose two methods that reduce the postnonlinear blind source separation problem (PNLBSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithma powerful technique from nonparametric statisticsto approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the soughtafter inverse functions of the nonlinearities. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACETD" and "GaussTD" on realistic examples are performed with excellent results.
Blind Source Separation Of Convolved Sources By Joint Approximate Diagonilzation of CrossSpectral Density Matrices
 Proceedings ICASSP 2001
, 2001
"... In this paper we present a new method for separating nonstationary sources from their convolutive mixtures based on approximate joint diagonalizing of the observed signals' crossspectral density matrices. Several blind source separation (BSS) algorithms have been proposed which use approximat ..."
Abstract

Cited by 23 (3 self)
 Add to MetaCart
(Show Context)
In this paper we present a new method for separating nonstationary sources from their convolutive mixtures based on approximate joint diagonalizing of the observed signals' crossspectral density matrices. Several blind source separation (BSS) algorithms have been proposed which use approximate joint diagonalization of a set of scalar matrices to estimate the instantaneous mixing matrix. We extend the concept of approximate joint diagonalization to estimate MIMO FIR channels. Based on this estimate we then design a separating network which will recover the original sources up to only a permutation and scaling ambiguity for minimum phase channels. We eliminate the commonly experienced problem of arbitrary scaling and permutation at each frequency bin, by optimizing the cost function directly with respect to the timedomain channel variables. We demonstrate the performance of the algorithm by computer simulations using real speech data. Speech samples are available at: http://sparky.mcmaster.ca/SSP/telephony kamran.htm.
A linear leastsquares algorithm for joint diagonalization
 In Proc. 4th Intern. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003
"... We present a new approach to approximate joint diagonalization of a set of matrices. The main advantages of our method are computational efficiency and generality. We develop an iterative procedure, called LSDIAG, which is based on multiplicative updates and on linear leastsquares optimization. The ..."
Abstract

Cited by 21 (5 self)
 Add to MetaCart
(Show Context)
We present a new approach to approximate joint diagonalization of a set of matrices. The main advantages of our method are computational efficiency and generality. We develop an iterative procedure, called LSDIAG, which is based on multiplicative updates and on linear leastsquares optimization. The efficiency of our algorithm is achieved by the firstorder approximation of the matrices being diagonalized. Numerical simulations demonstrate the usefulness of the method in general, and in particular, its capability to perform blind source separation without requiring the usual prewhitening of the data. 1.
Joint Diagonalization of Correlation Matrices by Using Gradient Methods with Application to Blind Signal Separation
 in Proc. SAM, 2002
, 2002
"... Joint diagonalization of several correlation matrices is a powerful tool for blind signal separation. This paper addresses the blind signal separation problem for the case where the source signals are nonstationary and / or nonwhite, and the sensors are possibly noisy. We present cost functions fo ..."
Abstract

Cited by 16 (0 self)
 Add to MetaCart
(Show Context)
Joint diagonalization of several correlation matrices is a powerful tool for blind signal separation. This paper addresses the blind signal separation problem for the case where the source signals are nonstationary and / or nonwhite, and the sensors are possibly noisy. We present cost functions for jointly diagonalizing several correlation matrices. The corresponding gradients are derived and used in a gradientbased jointdiagonalization algorithms. Several variations are given, depending on desired properties of the separation matrix, e.g., unitary separation matrix. These constraints are either imposed by adding a penalty term to the cost function or by projecting the gradient onto the desired manifold. The performance of the proposed jointdiagonalization algorithm is verified by simulating a blind signal separation application.