Problem statement: Independent Component Analysis (ICA) based algorithms applied in the context to remove the artifacts from the EEG signals are evaluated with appropriate metrics and it compares and contrasts the performance of the different methods for such applications. The primary goal is to gain some insight into relative performance of the various methods. Approach: CA is a statistical an...

The medical field along with computer field grows in a rapid manner. In telemedicine and telediagnosis authentication of medical images are very important to the patient ,doctor and Insurance company. The youngsters are also keen in knowing about the treatment methods and procedures. But no one likes to tell about the disease in open hall. So there is always an urge for watermarking of medical ...

The analysis of kinetic data monitored using spectroscopic techniques and its resolution into its unknown components is described. Independent Component Analysis (ICA) can be considered a calibration free technique with the outcome of the analyses being the spectral profiles of the unknown species. This enables the realisation of qualitative information concerning the identification of the numb...

HiPerSAT, a C++ library and associated tools, processes large EEG data sets with statistical data whitening and ICA (Independent Component Analysis) methods. The library uses BLAS, LAPACK, MPI and OpenMP to achieve a high performance solution that exploits available parallel hardware. ICA is a class of methods for analyzing a large set of data samples and deducing the independent components res...

In this paper, the linear (feed-forward) multilayer ICA algorithm is proposed for the blind separation of high-dimensional mixed signals. There are two main phases in each layer. One is the local ICA phase, where the mixed signals are divided into small local modules and a simple ICA is applied to each module. Another is the mapping phase, where the locally-separated signals are arranged as a l...

Many algorithms for independent component analysis (ICA) and blind source separation (BSS) can be considered particular instances of a criterion based on the sum of two terms: C(Y), which expresses the decorrelation of the components and G(Y), which measures their non-Gaussianity. Within this framework, the popular FastICA algorithm can be regarded as a technique that keeps C(Y) 1⁄4 0 by first ...

The FastICA or fixed point algorithm is one of the most successful algorithms for linear independent component analysis (ICA) in terms of accuracy and computational complexity. Two versions of the algorithm are available in literature and software: a one-unit (deflation) algorithm and a symmetric algorithm. The main result of this paper are analytic closed form expressions that characterize the...

In this paper we propose an information theory based generic method for complex Independent Component Analysis (ICA). Expressions for the complex score function are derived. The method exploits the full second order structure of complex signals. It combines a preprocessing step called the strong−uncorrelating transform (SUT) with ICA methods that use the proposed complex score function. The met...