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.

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.

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.

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.

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.