Complexity pursuit is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and independent component analysis (ICA). In this paper, we consider the estimation of the data model of ICA when Gaussian noise is present and the independent components are time dependent. We derive a simple algorithm combining Gau...

When applying independent component analysis (ICA), sometimes we expect that the connections between the observed mixtures and the recovered independent components (or the original sources) to be sparse, to make the interpretation easier or to reduce the model complexity. In this paper we propose natural gradient algorithms for ICA with a sparse separation matrix, as well as ICA with a sparse m...

Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address thi...

Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group differences and within-subject variability. We found that ICA diminished Leave-OneOut root mean square error (RMSE) of validation (from 0.32 to 0.28), indi...

This paper reports on numerical experiments on the ‘independent component analysis’ (ICA) of some nonGaussian stochastic processes. It is found that the orthonormal basis discovered by ICA are strikingly close to wavelet basis.

Independent Component Analysis (ICA) and Blind Source Separation (BSS) have become standard tools in multivariate data analysis. ICA continues to generate a flurry of research interest, resulting in increasing numbers of papers submitted to conferences and journals. Furthermore, there are many workshops and special sessions conducted in major conferences that focus on recent research results. T...

This paper addresses the blind source separation problem for the case where more sensors than source signals are available. A noisy-sensor model is assumed. The proposed algorithm comprises two stages, where the first stage consists of a principal component analysis (PCA) and the second one of an independent component analysis (ICA). The purpose of the PCA stage is to increase the input SNR of ...

In this work, we apply the independent component analysis (ICA) on the extraction of artifacts from the electrocardio-graphic (ECG) signals. ECG analysis is not an easy task when artifacts (electrodes, muscle, breathing, etc) corrupts the ECG, hiding important information. If the mixed signals in the ECG recordings (heart signal and artifacts) are statistically independent, the ICA can blindly ...

In this paper we improve a well known signal processing technique such as independent component analysis (ICA) or blind source separation applied to predicting multivariate financial such as portfolio of stock returns using the Vapnik-Chervonenkis theory. The key idea in ICA algorithms is to linearly map the input space series (stock returns) into a new space which contains statistically indepe...

This paper presents the application of independent component 10 analysis (ICA) for value at risk modelling (VaR). The probabilistic models 11 fitted to hidden components from the time series help to identify the 12 independent factors influencing the portfolio value. An important issue here 13 is the choice of the ICA algorithm, especially taking into account the 14 characteristics of the instr...