Independent component approach to the analysis of EEG and MEG recordings

Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoen- cephalographic (MEG) recordings. In addition, ICA has been ap- plied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field

Multi-modal dictionary learning for image separation with application in art investigation

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component models features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data - taken from digital acquisition of the Ghent Altarpiece (1432) - confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.Comment: submitted to IEEE Transactions on Images Processin

APPLICATION OF INDEPENDENT COMPONENT ANALYSIS FOR SOUND SOURCE SEPARATION

ABSTRACT In this paper, experiments on the application of the independent component analysis (ICA) technique to separate unknown source signals are reported. ICA is one of the fastest growing fields in signal processing with applications to speech recognition systems, telecommunications, and biomedical signal processing. It is a data-transformation technique that finds independent sources of activity from linear mixtures of unknown independent sources. The statistical method to measure independence is to find a linear representation of the non-Gaussian data so that the components are as independent as possible and the mutual information between them is minimum. Although extensive simulations have been performed to demonstrate the power of the learning algorithm for the problems of instantaneous mixing and un-mixing of sources, its application to the noise diagnosis and separation in an industrial setting has not been considered. Noise separation in machinery has a strong basis in the "cocktail problem" in which it is difficult to separate/isolate the voice of a person in a room filled with competing voices and noises. The experiments conducted consist of separating several artificially generated sources of noise. Our results demonstrate that ICA can be effectively employed for such kinds of applications. The underdetermined problem in which there are fewer sensors than sources in the ICA formulation is also examined by applying a time-invariant linear transformation of the acquired signals to identify a single source

Independent component analysis: algorithms and applications

A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject

Galactic planetary science.

Planetary science beyond the boundaries of our Solar System is today in its infancy. Until a couple of decades ago, the detailed investigation of the planetary properties was restricted to objects orbiting inside the Kuiper Belt. Today, we cannot ignore that the number of known planets has increased by two orders of magnitude nor that these planets resemble anything but the objects present in our own Solar System. Whether this fact is the result of a selection bias induced by the kind of techniques used to discover new planets-mainly radial velocity and transit-or simply the proof that the Solar System is a rarity in the Milky Way, we do not know yet. What is clear, though, is that the Solar System has failed to be the paradigm not only in our Galaxy but even 'just' in the solar neighbourhood. This finding, although unsettling, forces us to reconsider our knowledge of planets under a different light and perhaps question a few of the theoretical pillars on which we base our current 'understanding'. The next decade will be critical to advance in what we should perhaps call Galactic planetary science. In this paper, I review highlights and pitfalls of our current knowledge of this topic and elaborate on how this knowledge might arguably evolve in the next decade. More critically, I identify what should be the mandatory scientific and technical steps to be taken in this fascinating journey of remote exploration of planets in our Galaxy