The goal of this paper is to search for independent multidimensional processes subject to missing and mixed observations. The corresponding cocktail-party problem has a number of successful applications, however, the case of missing observations has been worked out only for the simplest Independent Component Analysis (ICA) task, where the hidden processes (i) are one-dimensional, and (ii) signa...

In this paper, an adaptive blind dereverberation method based on speech generative model is presented. Our ICA-based speech generative model can decompose speeches into independent sources. Experimental results show that the proposed blind dereverberation model successfully performs even in non-minimum phase channels.

The application of auditory steady–state responses (ASSRs) is an objective and frequency specific technique to assess reliable hearing thresholds at audiometric frequencies. Unfortunately, the duration of ASSR measurement can be long, which is unpractical for wide scale clinical application. Therefore, we propose independent component analysis (ICA) as a tool to improve the ASSR detection in re...

Most ICA algorithms are sensitive to outliers. Instead of robustifying existing algorithms by outlier rejection techniques, we show how a simple outlier index can be used directly to solve the ICA problem for super-Gaussian source signals. This ICA method is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed si...

We consider the problem of separating and determining the time courses of various synaptic input currents from simultaneous recordings of the time courses of membrane potentials, including spikes, of several neurons. Employing a suitable mathematical model, the method involves the differentiation of potentials and the use of ICA to determine the relative strengths of various synaptic inputs. At...

Independent component analysis (ICA) is a method to estimate components which are as statistically independent as possible. However, in many practical applications, the estimated components are not independent. Recent variants of ICA have made use of such residual dependencies to estimate an ordering (topography) of the components. Like in ICA, the components in those variants are assumed to be...

We present an application of Independent Component Analysis (ICA) to the discrimination of mental tasks for EEG-based Brain Computer Interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of ICA for direct discrimination of different types of EEG signals. By viewing ICA as a generative model, we can use Bayes’ rule to form a classifier. Thi...

In this paper we compare a machine based semantic organisation of natural images with the one provided by human perception. On one hand, we have conducted a psychophysical experiment to determine a human perception space in which we have identified semantic categories. These categories and the distances between images are emphasised by analysing the human response similarities with a multidimen...

We present an application of independent component analysis (ICA) to the discrimination of mental tasks for EEG-based brain computer interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of ICA for direct discrimination of different types of EEG signals. By viewing ICA as a generative model, we can use Bayes’ rule to form a classifier. We ...

In this paper, we propose an ICA(Indepdendent Component Analysis) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most PCA(Principal Component Analysis)-based methods have overcome illumination variation by discarding the projection to a few lead...