Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Robust Blind Source Separation Utilizing Second and Fourth Order Statistics
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Complex independent component analysis of frequency-domain electroencephalographic data
Neural Networks - Special issue: Neuroinformatics
Optimization techniques for independent component analysis with applications to EEG data
Quantitative neuroscience
The Journal of Machine Learning Research
Complex ICA using generalized uncorrelating transform
Signal Processing
On testing for impropriety of complex-valued Gaussian vectors
IEEE Transactions on Signal Processing
On the convergence of ICA algorithms with symmetric orthogonalization
IEEE Transactions on Signal Processing
Adaptive IIR filtering of noncircular complex signals
IEEE Transactions on Signal Processing
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
Noisy component extraction (NoiCE)
IEEE Transactions on Circuits and Systems Part I: Regular Papers
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Second-order statistics of complex signals
IEEE Transactions on Signal Processing
On Extending the Complex FastICA Algorithm to Noncircular Sources
IEEE Transactions on Signal Processing
Widely linear estimation with complex data
IEEE Transactions on Signal Processing
Complex random vectors and ICA models: identifiability, uniqueness, and separability
IEEE Transactions on Information Theory
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A class of second-order complex domain blind source extraction algorithms is introduced to cater for signals with noncircular probability distributions, which is a typical case in real-world scenarios. This is achieved by employing the so-called augmented complex statistics and based on the temporal structures of the sources, thus permitting widely linear (WL) predictability to be the extraction criterion. For rigor, the analysis of the existence and uniqueness of the solution is provided based on both the covariance and the pseudocovariance and for both noise-free and noisy cases, and serves as a platform for the derivation of the algorithms. Both direct solutions and those requiring prewhitening are provided based on a WL predictor, thus making the methodology suitable for the generality of complex signals (both circular and noncircular). Simulations on synthetic noncircular sources support the uniqueness and convergence study, followed by a real-world example of electrooculogram artifact removal from electroencephalogram recordings in real time.