An introduction to a new data analysis tool: Independent Component Analysis
Andreas Jung · University of Regensburg Publication Server (University of Regensburg) · 2002
A common problem encountered in data analysis and signal processing, is finding a suitable representation of multivariate data. For computational and conceptual simplicity, often these representations are sought as a linear transformation of the original data. Well known linear transformation are for example the principal component analysis or projection pursuit. A recently new developed nonlinear method is the independent component analysis (ICA), in which the components of the desired representation have minimal stochastical dependence. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we will focus on the theory and methods of ICA in contrast to classical transformations, as well as the applications of this method to biomedical data as for example electroencephalography (EEG). For an illustration of the algorithm, we will also visualized the unmixprocess with a set of images. Finally we will give an outlook to the possible future developments of ICA. Main aspects of my future research will be: using time structure information from the data to enhance the convergence of the algorithm; determine the meaningfulness of the independent components and treating non-stationary data as most biomedical systems are in a non-equilibrium. 1