Independent Component Analysis using an ExtendedInfomax Algorithm for Mixed Sub - Gaussian andSuper - Gaussian

An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able to blindly separate mixed signals with suband super-Gaussian source distributions. This was achieved by using a simple type of learning rule rst derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have suband super-Gaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between suband super-Gaussian regimes. We demonstrate that the extended infomax algorithm is able to easily separate 20 sources with a variety of source distributions. Applied to high-dimensional data from electroencephalographic (EEG) recordings, it is e ective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.