Abstract
We present the background for the statistical decomposition of a signal called independent component analysis (ICA) and survey its application to blind source separation (BSS). We review principal component analysis (PCA), and gradient and cumulant ICA techniques for the complete noiseless BSS problem (more sensors than sources). Results for noisy systems are also discussed. The application of these techniques in the analysis of biomedical signals like EEG, ECG and fMRI, and their early success, is reviewed. We also propose the separation of the current EEG and ECG electrical recordings into independent brain (iEEG) and heart signals (iECG) in order to provide better signals for compression, browsability, and noninvasive medical diagnosis.
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