PCA and ICA neural implementations for source separation - a comparative study

A comparative study of neural implementations running principal component analysis (PCA) and independent component analysis (ICA) was carried out. Both artificially generated data and real biomedical time series were employed in order to critically evaluate and assess the performance of various algorithms under study. The assumption of independence, even if weak, was proved reach in relevant interferences on brain activity. The ICA algorithms were proved more realistic in terms of neurophysiological relevance as compared to PCA.