The objective of this work was to develop bioadhesive topical gel of Aceclofenac with the help of response-surface approach. Experiments were performed according to a 3-level factorial design to evaluate the effects of two independent variables [amount of Poloxamer 407 (PL-407 = X1) and hydroxypropylmethyl cellulose K100 M (HPMC = X2)] on the bioadhesive character of gel, rheological property o...

We present a toolbox for exploratory analysis of functional magnetic resonance imaging (fMRI) data using independent component analysis (ICA) within the widely used SPM analysis pipeline. The toolbox enables dimensional reduction using principal component analysis, ICA using several different ICA algorithms, selection of the number of components using the Bayesian information criterion (BIC), v...

Artificially generated functional magnetic resonance imaging (fMRI) data drawn from a block-based visual stimulation paradigm were analyzed by the stochastic neuromorphic extended BS Infomax algorithm [1] implementing spatial independent component analysis (ICA) [2]. Variance estimate based on bootstrap resampling [3] was employed as model selection criterion and reliability assessment of ICA d...

In the absence of an explicit task, temporal synchrony is maintained across brain regions. Taking advantage of this synchrony, resting-state fMRI has been used extensively to identify resting state networks (RSN) [1]. Fox et al. have reported that the default mode network (DMN) is anti-correlated with the task positive network (TPN) [2], reflecting the competing demands of these two networks. T...

High-dimensional data, such as images represented as points in the space spanned by their pixel values, can often be described in a significantly smaller number of dimensions than the original. One of the ways of finding lowdimensional representations is to train a mixture model of principal component analysers (PCA) on the data. However, some types of data do not fulfill the assumptions of PCA...

In the paper, we present a comparative study of several methods used nowadays in the field of feature and information extraction. We compared several Independent Component Analysis (ICA) algorithms together with the commonly used Principal Component Analysis (PCA) algorithm in two real-world tasks. The first task was a Voice Activity Detection (VAD), the second is Speaker Verification and Recog...

The data model of independent component analysis (ICA) gives a multivariate probability density that describes many kinds of sensory data better than classical models like Gaussian densities or Gaussian mixtures. When only a subset of the random variables is observed, ICA can be used for regression, i.e. to predict the missing observations. In this paper, we show that the resulting regression i...

We present an unsupervised classification algorithm based on an ICA mixture model. The ICA mixture model assumes that the observed data can be categorized into several mutually exclusive data classes in which the components in each class are generated by a linear mixture of independent sources. The algorithm finds the independent sources, the mixing matrix for each class and also computes the c...