Abstract
A new class of complex domain blind source extraction algorithms suitable for the extraction of both circular and non-circular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of non-circular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources.
Highlights
The aim of blind source separation (BSS) is to reconstruct the original sources by identifying the inverse of the mixing system, without having explicit knowledge of the mixing parameters or sources (Cichocki and Amari, 2002), and has found application in diverse areas including biomedical engineering, communications, sonar, and radar (Cichocki and Amari, 2002; Anemüller et al, 2003)
Artifacts arising from eye rolling and raising the eye brow might seem similar to that of an eye blink, it is much more challenging to perform their complete removal in the context of real-time EEG processing, as they involve longer firing of larger groups of muscles
5 Conclusion Blind source extraction of the generality of complex-valued signals based on the degree of non-Gaussianity and from noisy mixtures has been addressed
Summary
The aim of blind source separation (BSS) is to reconstruct the original sources by identifying the inverse of the mixing system, without having explicit knowledge of the mixing parameters or sources (Cichocki and Amari, 2002), and has found application in diverse areas including biomedical engineering, communications, sonar, and radar (Cichocki and Amari, 2002; Anemüller et al, 2003). A class of BSS algorithms, termed blind source extraction (BSE), aims to retrieve the sources one by one, based on a fundamental signal property (non-linearity, sparsity), effectively inducing an order into the separation process. The benefit of BSE becomes apparent in large-scale problems where only a small subset of the sources are of interest, making it possible to extract such sources at a dramatically reduced computational complexity and in real-time. This relaxes the requirement for pre- or post-processing of the mixture or separated sources, that may be necessary if parallel BSS techniques were employed
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