Ricardo Vigário

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 magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field.

Extraction of ocular artefacts from EEG using independent component analysis.

Eye activity is one of the main sources of artefacts in EEG and MEG recordings. A new approach to the correction of these disturbances is presented using the statistical technique of independent component analysis. This technique separates components by the kurtosis of their amplitude distribution over time, thereby distinguishing between strictly periodical signals, regularly occurring signals and irregularly occurring signals. The latter category is usually formed by artefacts. Through this approach, it is possible to isolate pure eye activity in the EEG recordings (including EOG channels), and so reduce the amount of brain activity that is subtracted from the measurements, when extracting portions of the EOG signals.

A class of neural networks for independent component analysis

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 related to multilayer feedforward networks for performing complete ICA. The basic ICA network consists of whitening, separation, and basis vector estimation layers. It can be used for both blind source separation and estimation of the basis vectors of ICA. We consider learning algorithms for each layer, and modify our previous nonlinear PCA type algorithms so that their separation capabilities are greatly improved. The proposed class of networks yields good results in test examples with both artificial and real-world data.

Neural networks for blind separation with unknown number of sources

Blind source separation problems have recently drawn a lot of attention in unsupervised neural learning. In the current approaches, the number of sources is typically assumed to be known in advance, but this does not usually hold in practical applications. In this paper, various neural network architectures and associated adaptive learning algorithms are discussed for handling the cases where the number of sources is unknown. These techniques include estimation of the number of sources, redundancy removal among the outputs of the networks, and extraction of the sources one at a time. Validity and performance of the described approaches are demonstrated by extensive computer simulations for natural image and magnetoencephalographic (MEG) data. ( 1999 Elsevier Science B.V. All rights reserved.

Applications of neural blind separation to signal and image processing

In blind source separation one tries to separate statistically independent unknown source signals from their linear mixtures without knowing the mixing coefficients. Such techniques are currently studied actively both in statistical signal processing and unsupervised neural learning. We apply neural blind separation techniques developed in our laboratory to the extraction of features from natural images and to the separation of medical EEG signals. The new analysis method yields features that describe the underlying data better than for example classical principal component analysis. We discuss difficulties related with real-world applications of blind signal processing, too.

Extraction of event-related signals from multichannel bioelectrical measurements

Independent component analysis (ICA) is a powerful tool for separating signals from their mixtures. In this field, many algorithms were proposed, but they poorly use a priori information in order to find the desired signal. Here, we propose a fixed point algorithm which uses a priori information to find the signal of interest out of a number of sensors. We particularly applied the algorithm to cancel cardiac artifacts from a magnetoencephalogram.

BSS and ICA in Neuroinformatics: From Current Practices to Open Challenges

We give a general overview of the use and possible misuse of blind source separation (BSS) and independent component analysis (ICA) in the context of neuroinformatics data processing. A clear emphasis is given to the analysis of electrophysiological recordings, as well as to functional magnetic resonance images (fMRI). Two illustrative examples include the identification and removal of artefacts in both kinds of data, and the analysis of a simple fMRI. A second part of the paper addresses a set of currently open challenges in signal processing. These include the identification and analysis of independent subspaces, the study of networks of functional brain activity, and the analysis of single-trial event-related data.

Analyzing consistency of independent components: An fMRI illustration

Independent component analysis (ICA) is a powerful data-driven signal processing technique. It has proved to be helpful in, e.g., biomedicine, telecommunication, finance and machine vision. Yet, some problems persist in its wider use. One concern is the reliability of solutions found with ICA algorithms, resulting from the stochastic changes each time the analysis is performed. The consistency of the solutions can be analyzed by clustering solutions from multiple runs of bootstrapped ICA. Related methods have been recently published either for analyzing algorithmic stability or reducing the variability. The presented approach targets the extraction of additional information related to the independent components, by focusing on the nature of the variability. Practical implications are illustrated through a functional magnetic resonance imaging (fMRI) experiment.

Independence: a new criterion for the analysis of the electromagnetic fields in the global brain?

The impressive increase in the understanding of some basic processing in the human brain has recently led to the formulation of efficient computational methods, which when applied in the design of better signal processing tools, provides a deeper and clearer view to study the functioning of the human brain. The recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic and magnetoencephalographic recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. Extensions of the basic ICA methodology have also been employed to reveal otherwise hidden information. This paper reviews our recent results in this field.

Nonlinear PCA type approaches for source separation and independent component analysis

Studies the application of some nonlinear neural pricipal component analysis (PCA) type approaches to the separation of independent source signals from their linear mixture. This problem is important in signal processing and communications, and it cannot be solved using standard PCA. Using prewhitening and appropriate choice of nonlinearities, several algorithms proposed by the authors yield good separation results for sub-Gaussian (or super-Gaussian) source signals. The authors discuss the related problem of estimating the basis vectors in independent component analysis briefly, too.