  The extended infomax algorithm for independent component analysis (ICA) can
separate sub- and super-Gaussian signals but converges slowly as it uses
stochastic gradient optimization. In this paper, an improved extended infomax
algorithm is presented that converges much faster. Accelerated convergence is
achieved by replacing the natural gradient learning rule of extended infomax by
a fully-multiplicative orthogonal-group based update scheme of the unmixing
matrix leading to an orthogonal extended infomax algorithm (OgExtInf).
Computational performance of OgExtInf is compared with two fast ICA algorithms:
the popular FastICA and Picard, a L-BFGS algorithm belonging to the family of
quasi-Newton methods. Our results demonstrate superior performance of the
proposed method on small-size EEG data sets as used for example in online EEG
processing systems, such as brain-computer interfaces or clinical systems for
spike and seizure detection.
