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Andrzej Cichocki

Publications and source records attributed to Andrzej Cichocki.

17 recordsLinked to original sources
PubMed2006

Speech comprehension assessed by electroencephalography: a new method using m-sequence modulation.

Electroencephalograms (EEGs) were recorded from eight Japanese speakers while they listened to Japanese and Spanish sentences (approximately 51s each). The sentences were modulated in amplitude by a binary m-sequence and played forward or backward. A circular cross-correlation function was computed between the EEG signals and the m-sequence and averaged across subjects. Independent component analysis of the averaged function revealed a component source response which was obtained only for the comprehensible Japanese and not for the incomprehensible sentences. The present study has thus shown that a 1-min long EEG signal is sufficient for the assessment of speech comprehension.

Acoustic Stimulation↗
PubMed2006

Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis.

Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been studied to identify areas related to EEG events. EEG data recorded in the magnetic resonance (MR) scanner with MR imaging is suffered from two specific artifacts, imaging artifact, and ballistocardiogram (BCG). In this paper, we focus on BCG. In preceding studies, average subtraction was often used for this purpose. However, average subtraction requires an assumption that BCG waveforms are precisely periodic, which seems unrealistic because BCG is a biomedical artifact. We propose the application of independent component analysis (ICA) with a postprocessing of high-pass filtering for the removal of BCG. With this approach, it is not necessary to assume that the BCG waveform is periodic. Empirically, we show that our proposed method removes BCG artifacts as well as does the average subtraction method. Power spectral density analysis of the two approaches shows that, with ICA, distortion of recovered EEG data is also as small as that associated with the average subtraction approach. We also propose a hypothesis for how head movement causes BCGs and show why ICA can remove BCG artifacts arising from this source.

Algorithms↗
PubMed2006

Blind estimation of channel parameters and source components for EEG signals: a sparse factorization approach.

In this paper, we use a two-stage sparse factorization approach for blindly estimating the channel parameters and then estimating source components for electroencephalogram (EEG) signals. EEG signals are assumed to be linear mixtures of source components, artifacts, etc. Therefore, a raw EEG data matrix can be factored into the product of two matrices, one of which represents the mixing matrix and the other the source component matrix. Furthermore, the components are sparse in the time-frequency domain, i.e., the factorization is a sparse factorization in the time frequency domain. It is a challenging task to estimate the mixing matrix. Our extensive analysis and computational results, which were based on many sets of EEG data, not only provide firm evidences supporting the above assumption, but also prompt us to propose a new algorithm for estimating the mixing matrix. After the mixing matrix is estimated, the source components are estimated in the time frequency domain using a linear programming method. In an example of the potential applications of our approach, we analyzed the EEG data that was obtained from a modified Sternberg memory experiment. Two almost uncorrelated components obtained by applying the sparse factorization method were selected for phase synchronization analysis. Several interesting findings were obtained, especially that memory-related synchronization and desynchronization appear in the alpha band, and that the strength of alpha band synchronization is related to memory performance.

Adult↗
PubMed2006

1,111 patients with adrenal incidentalomas observed at a single endocrinological center: incidence of chromaffin tumors.

A majority of incidentally found adrenal tumors derive from the adrenal cortex. The aim of our study was evaluation of the incidence of chromaffin tumors in a group of 1,111 patients with adrenal incidentalomas. In this group there were 803 women and 308 men, aged 10-87 years. Clinical examination, imaging studies (ultrasound scans, CT, and MRI if necessary), and hormonal determinations (cortisol, ACTH and androgens in the blood, dexamethasone suppression test, urinary excretion of 17-OHCS, aldosterone and 17-KS, as well as PRA/aldosterone stimulation test and metanephrines in hypertensive patients and those with density in CT over 20 HU) were used. In 380 patients treated by surgery (mainly by laparoscopic approach), histological and immunocytochemical examinations were performed. Clinical examination revealed hypertension in 25% of the patients under study. Chromaffin tumors were detected in 43 patients, 33 women, and 10 men aged 20-75 years: pheochromocytoma in 36 (malignant in 3); chromaffin cells hyperplasia in 2; paraganglioma in 3; ganglioneuroblastoma in 1; ganglioneuroma in 1; and schwannoma in 2. The tumors' diameter ranged between 1.1 and 20.0 cm, density=25 Hounsfield units (HU) or more before contrast medium injection. Hypertension was present in 53% of these patients. The urinary metanephrines excretion was elevated in 31 of 38 patients, in whom the determinations were done. Chromaffin tumors were detected in 4% (pheochromocytomas in 3%) of 1,111 patients with adrenal incidentalomas. Malignancy was present in 9% of 43 patients with chromaffin tumors. High density in CT was a very important diagnostic finding in the incidentally found medullary tumors.

Adolescent↗
PubMed2005

EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease.

OBJECTIVE: Development of an EEG preprocessing technique for improvement of detection of Alzheimer's disease (AD). The technique is based on filtering of EEG data using blind source separation (BSS) and projection of components which are possibly sensitive to cortical neuronal impairment found in early stages of AD. METHODS: Artifact-free 20s intervals of raw resting EEG recordings from 22 patients with Mild Cognitive Impairment (MCI) who later proceeded to AD and 38 age-matched normal controls were decomposed into spatio-temporally decorrelated components using BSS algorithm 'AMUSE'. Filtered EEG was obtained by back projection of components with the highest linear predictability. Relative power of filtered data in delta, theta, alpha 1, alpha 2, beta 1, and beta 2 bands were processed with Linear Discriminant Analysis (LDA). RESULTS: Preprocessing improved the percentage of correctly classified patients and controls computed with jack-knifing cross-validation from 59 to 73% and from 76 to 84%, correspondingly. CONCLUSIONS: The proposed approach can significantly improve the sensitivity and specificity of EEG based diagnosis. SIGNIFICANCE: Filtering based on BSS can improve the performance of the existing EEG approaches to early diagnosis of Alzheimer's disease. It may also have potential for improvement of EEG classification in other clinical areas or fundamental research. The developed method is quite general and flexible, allowing for various extensions and improvements.

Aged↗
PubMed2005

Sparse component analysis and blind source separation of underdetermined mixtures.

In this letter, we solve the problem of identifying matrices S is an element of R(n x N) and A is an element of R(m x n) knowing only their multiplication X = AS, under some conditions, expressed either in terms of A and sparsity of S (identifiability conditions), or in terms of X (sparse component analysis (SCA) conditions). We present algorithms for such identification and illustrate them by examples.

Algorithms↗
PubMed2005

Clinical role of determination of plasma mitotane and its metabolites levels in patients with adrenal cancer: results of a long-term follow-up.

Our study aimed at evaluation of the relations between the plasma levels of mitotane (o,p'-DDD) and its metabolites, o,p'-DDA and o,p'-DDE, and the efficacy of Mitotane therapy during a long-term follow-up. Eighteen patients, aged 11 to 70 years, were included to the study. Metastatic or regional stage was diagnosed in 15 patients, while localized disease in three patients. Mitotane has been administered in daily doses of 3.0 to 10.0 g in metastatic and regional stages, and 1.5 to 4.0 g in the localized disease, simultaneously with hydrocortisone, prednisolone and fludrocortisone. Mitotane and its metabolites were determined by a high-pressure liquid chromatographic method. The plasma o,p'-DDD level exceeding 44 _M/L, considered as curative one, was reached in nine cases. The o,p'-DDA/o,p'-DDD ratio rose significantly mainly in the first 1-3 months of therapy. The o,p'-DDE levels rose slowly, reaching higher values in long-term therapy, over 12 months of mitotane administration. In the group of patients with regional or metastatic stage, both the o,p'-DDE levels and the o,p'-DDE/o,p'-DDD ratios were higher in the survivors than in non-survivors. The results of our study suggest that the plasma concentrations of o,p'-DDE were more closely related to clinical improvement or remission than the o,p'-DDD levels.

Adolescent↗
PubMed2004

Self-adaptive blind source separation based on activation functions adaptation.

Independent component analysis is to extract independent signals from their linear mixtures without assuming prior knowledge of their mixing coefficients. As we know, a number of factors are likely to affect separation results in practical applications, such as the number of active sources, the distribution of source signals, and noise. The purpose of this paper to develop a general framework of blind separation from a practical point of view with special emphasis on the activation function adaptation. First, we propose the exponential generative model for probability density functions. A method of constructing an exponential generative model from the activation functions is discussed. Then, a learning algorithm is derived to update the parameters in the exponential generative model. The learning algorithm for the activation function adaptation is consistent with the one for training the demixing model. Stability analysis of the learning algorithm for the activation function is also discussed. Both theoretical analysis and simulations show that the proposed approach is universally convergent regardless of the distributions of sources. Finally, computer simulations are given to demonstrate the effectiveness and validity of the approach.

Computer Simulation↗
PubMed2004

From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability.

This paper reports a study on the problem of the blind simultaneous extraction of specific groups of independent components from a linear mixture. This paper first presents a general overview and unification of several information theoretic criteria for the extraction of a single independent component. Then, our contribution fills the theoretical gap that exists between extraction and separation by presenting tools that extend these criteria to allow the simultaneous blind extraction of subsets with an arbitrary number of independent components. In addition, we analyze a family of learning algorithms based on Stiefel manifolds and the natural gradient ascent, present the nonlinear optimal activations (score) functions, and provide new or extended local stability conditions. Finally, we illustrate the performance and features of the proposed approach by computer-simulation experiments.

Algorithms↗
PubMed2004

Analysis of sparse representation and blind source separation.

In this letter, we analyze a two-stage cluster-then-l(1)-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l(1) norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l(1)-norm solution and the l(0)-norm solution is also analyzed according to a probabilistic framework. If the obtained l(1)-norm solution is sufficiently sparse, then it is equal to the l(0)-norm solution with a high probability. Furthermore, the l(1)- norm solution is robust to noise, but the l(0)-norm solution is not, showing that the l(1)-norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed domain and with the case in which the source number is unknown. It is also robust to additive noise and estimation error in the mixing matrix. Finally, four simulation examples and an EEG data analysis example are presented to illustrate the algorithm's utility and demonstrate its performance.

Algorithms↗
PubMed2003

Wavelet-like receptive fields emerges by non-linear minimization of neuron error.

Redundancy reduction as a form of neural coding has been since the early sixties a topic of large research interest. A number of strategies has been proposed, but the one which is attracting most attention recently assumes that this coding is carried out so that the output signals are mutually independent. In this work we go one step further and suggest an strategy to deal also with non-orthogonal signals (i.e., "dependent" signals). Moreover, instead of working with the usual squared error, we design a neuron where the non-linearity is operating on the error. It is computationally more economic and, importantly, the permutation/scaling problem is avoided. The framework is given with a biological background, as we avocate throughout the manuscript that the algorithm fits well the single neuron and redundancy reduction doctrine. Moreover, we show that wavelet-like receptive fields emerges from natural images processed by this algorithm.

Algorithms↗
PubMed2002

Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis.

In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented. In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction. ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach.

Acoustic Stimulation↗
PubMed2002

Equivariant nonstationary source separation.

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. [Neural Networks 8 (1995) 411] and derive natural gradient learning algorithms for both fully connected recurrent network and feedforward network. Since our algorithms employ the natural gradient method, they possess the equivariant property and find a steepest descent direction unlike the algorithm [Neural Networks 8 (1995) 411]. We also show that our algorithms are always locally stable, regardless of probability distributions of nonstationary sources.

Algorithms↗
PubMed1997

A Minor Component Analysis Algorithm.

The eigenvectors corresponding to the smallest eigenvalues of the autocorrelation matrix of the input signals are defined as the minor components, which play a very important role in many fields of adaptive signal processing such as spectral estimation, total least squares processing, eigen-based bearing estimation, digital beamforming, moving target indication, and clutter cancellation. This paper proposes a learning algorithm which extracts adaptively the minor component. We will use the Rayleigh quotient as an energy function and prove both analytically and by simulation results that the weight vector provided by the proposed algorithm is guaranteed to converge to the minor component of the input signals. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Journal Article↗
PubMed1997

Stability Analysis of Learning Algorithms for Blind Source Separation.

Recently a number of adaptive learning algorithms have been proposed for blind source separation. Although the underlying principles and approaches are different, most of them have very similar forms. Two important issues remained to be elucidated further: the statistical efficiency and the stability of learning algorithms. The present letter analyzes a general form of statistically efficient algorithms and gives a necessary and sufficient condition for the separating solution to be a stable equilibrium of a general learning algorithm. Moreover, when the separating solution is unstable, a simple method is given for stabilizing the separating solution by modifying the algorithm.

Journal Article↗
PubMedDate not supplied

[Gynecomasty as a first sign of adrenal carcinoma--case report].

We present a case of 50 year-old man with feminizing adrenal carcinoma. He was admitted to the hospital because of weakness and one year history of gynecomastia and high blood pressure. Examinations revealed a large left adrenal mass and increased levels of estradiol. Patient underwent adrenalectomy and followed by mitotan therapy as the result of histopathological examination was adrenocortical carcinoma. One year after operation patient stays free from the recurrence of the disease and his estradiol, androstendion and DHEA levels are below the detection limits. We report this case because feminizing adrenal carcinoma is a very rare but serious disease and gynecomastia that could be its manifestation is quite frequent symptom in men's population and thus it could easily be missed. In every case of gynecomastia related to estradiol excess feminizing tumors of testis and adrenal gland should be ruled out.

Adrenal Cortex Neoplasms↗