Abstract
Independent Components Analysis has recently become an important tool for modelling and understanding empirical datasets. In this chapter we review the theoretical basis of ICA, outline an approach to non-stationary ICA, and describe a number of biomedical case studies. ICA is discussed in the framework of general linear models, which permits comparison with less general methods, such as principal components analysis, and with flexible models, such as neural networks.
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© 2000 Springer-Verlag London
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Everson, R., Roberts, S.J. (2000). Independent Components Analysis. In: Lisboa, P.J.G., Ifeachor, E.C., Szczepaniak, P.S. (eds) Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0487-2_13
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DOI: https://doi.org/10.1007/978-1-4471-0487-2_13
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