Abstract
Independent component analysis (ICA) is a powerful tool for separating signals from their observed mixtures. This area of research has produced many varied algorithms and approaches to the solution of this problem. The majority of these methods adopt a truly blind approach and disregard available a priori information in order to extract the original sources or a specific desired signal. In this contribution we propose a fixed point algorithm which utilizes a priori information in finding a specified signal of interest from the sensor measurements. This technique is applied to the extraction and channel isolation of sleep spindles from a multi-channel electroencephalograph (EEG).
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© 2000 Springer-Verlag London
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Barros, A.K., Rosipal, R., Girolami, M., Dorffner, G., Ohnishi, N. (2000). Extraction of Sleep-Spindles from the Electroencephalogram (EEG). In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_17
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_17
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