Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal ord...

We introduce FuncICA, a new independent component analysis method for pattern discovery in inherently functional data, such as time series data. We show how applying the dual of temporal ICA to temporal data, and likewise applying the dual of spatiotemporal ICA to spatiotemporal data, enables independent component regularization not afforded by the primal forms applied to their original domains...

The convex divergence is used as a surrogate function for obtaining a class of ICA algorithms (Independent Component Analysis) called the f-ICA. The convex divergence is a super class of α-divergence, which is a further upper family of Kullback-Leibler divergence or mutual information. Therefore, the f-ICA contains the α-ICA and the minimum mutual information ICA. In addition to theoretical int...

This study investigates the potential of independent component analysis (ICA) to provide a data-driven approach for group level analysis of magnetic resonance (MR) spectra. ICA collectively analyzes data to identify maximally independent components, each of which captures covarying resonances, including those from different metabolic sources. A comparative evaluation of the ICA approach with th...

Independent component analysis (ICA) is a technique that separates the independent source signals from their mixtures by minimizing the statistical dependence between components. This paper presents a floating point implementation of a novel fast confluence adaptive independent component analysis (FCAICA) technique with reduced number of iterations that provides the high convergence speed. Fixe...

Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOMICA) to linear geometric ICA using a self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing toget...

We present a method to deal with adaptive noise cancelling based on independent component analysis (ICA). Although popular least-mean-squares (LMS) algorithm removes noise components based on second-order correlation, the proposed ICA-based algorithm can utilize higher-order statistics. Additionally, extending to transform-domain adaptive filtering (TDAF) methods, normalized ICA-based algorithm...

In this study, blind code extraction of Direct Sequence Code Division Multiple Access (DS-CDMA) signals is considered, based on Independent Component Analysis (ICA) method. In order to distinguish between correct and incorrect extracted codes, to estimate the number of active users and also to determine the quality of detection along with the ICA based blind detection procedure, some propositio...

Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis is used for dimension reduction prior to ICA (PCA+ICA), which could remove important information. The problem is that interesting independent components (ICs) could be mixed in several principal components th...

Independent component analysis (ICA) is a recently developed, useful extension of standard principal component analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures. In this application only the source signals which correspond to the coefficients of the ICA expansion are of interest. In this paper, we propose neural structures ...