A fast fixed-point algorithm for independent component analysis
Neural Computation
A new adaptive scheme for ECG enhancement
Signal Processing
Complex independent component analysis of frequency-domain electroencephalographic data
Neural Networks - Special issue: Neuroinformatics
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
Activity index variance as an indicator of the number of signal sources
WSEAS Transactions on Signal Processing
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In this paper we compare the performance of different algorithms employed in solving frequency domain blind source separation of convolutive mixtures. The convolutive model is an extension of the instantaneous one and it allows to relax the hypothesis of a linear mixing process in which all the sources are supposed to reach the electrodes at the same time. This test is carried out in the frequency domain, where the algorithms developed for independent component analysis can be employed with minor modifications. The decomposition performance of such algorithms is evaluated on simulated dataset of convultive mixtures of biomedical signals.