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The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals including electrocardiogram (ECG), electromyogram (EMG) and 60-Hz sinusoid are linearly mixed for experimental tests. The mean-square errors (MSE) between the original sources and the separated...
In biomedical signal processing, it is often the case that many sources are mixed into the measured signal. The goal is usually to analyze one or several of them separately. In the case of multichannel measurements, several blind source separation techniques are available for decomposing the signal into its components [e.g., independent component analysis (ICA)]. However, only a few techniques have...
This paper reports analysis of the limitations of using independent component analysis (ICA) for biosignal analysis especially artefact removal. The possible difficulty is that there are limited number of electrodes (recordings) making it an overcomplete problem (non-square ICA). The other difficulty is the distribution of biosignal being close to Gaussian. These two properties of the signals may...
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