An Efficient Jacobi-Like Deflationary ICA Algorithm: Application to EEG Denoising

In this paper, we propose a Jacobi-like Deflationary ICA algorithm, named JDICA. More particularly, while a projection-based deflation scheme inspired by Delfosse and Loubaton´s ICA technique ( DelL^BBR) is used, a Jacobi-like optimization strategy is proposed in order to maximize a fourth order cumulant-based contrast built from whitened observations. Experimental results obtained from simulated epileptic EEG data mixed with a real muscular activity and from the comparison in terms of performance and numerical complexity with the FastICA, RobustICA and DelL^BBR algorithms, show that the proposed algorithm offers the best trade-off between performance and numerical complexity when a low number ( ~ 12) of electrodes is available.