Wai Yie Leong

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We provide an overview of blind source extraction (BSE) algorithms whereby only one source of interest is separated at the time. First, BSE approaches for linear instantaneous mixtures are reviewed with a particular focus on the ''linear predictor'' based approach. A rigorous proof of the existence BSE paradigm is provided, and the mean-square prediction(More)
Existing blind source extraction (BSE) methods are limited to noise-free mixtures, which is not realistic. We therefore address this issue and propose an algorithm based on the normalised kur-tosis and a nonlinear predictor within the BSE structure, which makes this class of algorithms suitable for noisy environments, a typical situation in practice. Based(More)
—An extension of blind source extraction (BSE) of one or a group of sources to the case of ill—conditioned and post-non-linear (PNL) mixing is introduced. This is achieved by a " mixed objective " type of cost function which jointly maximizes the kur-tosis of a recovered source and estimates a measure of nonlinearity within the mixing system. This helps to(More)
An adaptive biosignal analysis method for the detection and extraction of microsleep events is presented. We proposed a blind source extraction method applying Kalman filtering to extract the microsleep events. This is achieved by an adaptive algorithm in which the cost function jointly estimates the kur-tosis and a measure of nonlinearity. Next, Kalman(More)