Blind separation of convolved sources using the independent component analysis and information maximization approach

Independent Component Analysis (ICA) is very closely related to the method called blind source separation (BSS) or blind signal separation. In Independent Component Analysis (ICA) components are assumed statistically independent which we call independent source signal. In our thesis we have considered only noiseless ICA case. In a number of real-world signal processing applications, signals from various independent sources may get distorted by environmental factors that can be represented as convolutive mixtures of original signals received at the sensors. In this thesis, the effects of environmental factors and modeling assumptions on the performance capabilities of independent component analysis-based techniques are investigated. The so-called blind source separation feedback network architecture that is capable of coping with convolutive mixtures of sources is derived using Bell and Sejnowski's information maximization principle

Source Separation using ICA

Independent Component Analysis (ICA) is a statistical signal processing technique having emerging new practical application areas, such as blind signal separation such as mixed voices or images, analysis of several types of data or feature extraction. Fast independent component analysis (Fast ICA ) is one of the most efficient ICA technique. Fast ICA algorithm separates the independent sources from their mixtures by measuring non-gaussianity.In this paper we present a method that can separate the signals as individual channels from other channels and also remove the noise using fast ica algorithm. The method is to decompose a multi channel signal into statistically independent components

Second order statistics based blind source separation for artifact correction of short ERP epochs

ERP is commonly obtained by averaging over segmented EEC epochs. In case artifacts are present in the raw EEC measurement, pre-processing is required to prevent the averaged ERP waveform being interfered by artifacts. The simplest pre-processing approach is by rejecting trials in which presence of artifact is detected. Alternatively artifact correction instead of rejection can be performed by blind source separation, so that waste of ERP trials is avoided. In this paper, we propose a second order statistics based blind source separation approach to ERP artifact correction. Comparing with blind separation using independent component analysis, second order statistics based method does not rely on higher order statistics or signal entropy, and therefore leads to more robust separation even if only short epochs are available.published_or_final_versio

Efficient independent component analysis

Independent component analysis (ICA) has been widely used for blind source separation in many fields such as brain imaging analysis, signal processing and telecommunication. Many statistical techniques based on M-estimates have been proposed for estimating the mixing matrix. Recently, several nonparametric methods have been developed, but in-depth analysis of asymptotic efficiency has not been available. We analyze ICA using semiparametric theories and propose a straightforward estimate based on the efficient score function by using B-spline approximations. The estimate is asymptotically efficient under moderate conditions and exhibits better performance than standard ICA methods in a variety of simulations.Comment: Published at http://dx.doi.org/10.1214/009053606000000939 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org