This communication aims at giving some insights into the use of Independent Component Analysis (ICA) for solving biomedical problems. First the concept of ICA is reviewed and different classes of ICA methods are described. Next a survey on most encountered biomedical problems solved using ICA is detailed. Finally a comparative performance study of thirteen ICA algorithms is performed on biomedical

Evoked and coordinated brain signals often exhibit distinct, individualized spatial and temporal characteristics, such as amplitude and phase couplings across and within spatial channels. In the study of these brain potentials, it is important to characterize both the spatial and temporal morphologies of the responses for a better understanding of both the physiology and function of the brain. ...

(Note that for circuits we implicitly assume that ∅ / ∈ C(M), just as we assume that for matroids ∅ ∈ I.) Proof: 1. follows from the definition that a circuit is a minimally dependent set, and therefore a circuit cannot contain another circuit. 2. Let X, Y ∈ C(M) where X 6= Y , and e ∈ X ∩ Y . From 1, it follows that X \ Y is non-empty; let f ∈ X \ Y . Assume on the contrary that (X ∪ Y ) − e i...

Procedures such as FOBI that jointly diagonalize two matrices with the independence property have a long tradition in ICA. These procedures have well-known statistical properties, for example they are prone to failure if the sources have multiple identical values on the diagonal. In this paper we suggest to diagonalize jointly k ≥ 2 scatter matrices having the independence property. For the joi...

In this paper, we describe an approach to camera metadata estimation using regression based on Independent Component Analysis (ICA). Semantic scene classification of images using camera metadata related to capture conditions has had some success in the past. However, different makes and models of camera capture different types of metadata and this severely hampers the application of this kind o...

Geometric algorithms for linear independent component analysis (ICA) have recently received some attention due to their pictorial description and their relative ease of implementation. The geometric approach to ICA has been proposed first by Puntonet and Prieto [6] in order to separate linear mixtures. One major drawback of geometric algorithms is, however, an exponentially rising number of sam...

In this article, we test a word vector space model using direct evaluation methods. We show that independent component analysis is able to automatically produce meaningful components that correspond to semantic category labels. We also study the amount of features needed to represent a category using feature selection with syntactic and semantic category test sets.

This paper introduces an interactive classification technique for volume data, called ISpace, which uses Independent Component Analysis (ICA) and a multidimensional histogram of the volume data in a transformed space. Essentially, classification in the volume domain becomes equivalent to interactive clipping in the ICA space, which as demonstrated using several examples is more intuitive and di...

In meetings or noisy public places, often a number of speakers are active simultaneously and the sources of interest need to be separated from interfering speech in order to be robustly recognized. Independent component analysis (ICA) has proven to be a valuable tool for this purpose. However, under difficult environmental conditions, ICA outputs may still contain strong residual components of ...

We present a novel extension to Independent Component Analysis (ICA), where the data is generated as the product of two submodels, each of which follow an ICA model, and which combine in a horizontal fashion. This is in contrast to previous nonlinear extensions to ICA which were based on a hierarchy of layers. We apply the product model to natural image patches and report the emergence of local...