Abstract
This paper presents a new application of independent component analysis mixture modeling (ICAMM) for prediction of electroencephalographic (EEG) signals. Demonstrations in prediction of missing EEG data in a working memory task using classic methods and an ICAMM-based algorithm are included. The performance of the methods is measured by using four error indicators: signal-to-interference (SIR) ratio, Kullback-Leibler divergence, correlation at lag zero and mean structural similarity index. The results show that the ICAMM-based algorithm outperforms the classical spherical splines method which is commonly used in EEG signal processing. Hence, the potential of using mixtures of independent component analyzers (ICAs) to improve prediction, as opposed on estimating only one ICA is demonstrated.
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Safont, G., Salazar, A., Vergara, L., Gonzalez, A., Vidal, A. (2012). Mixtures of Independent Component Analyzers for EEG Prediction. In: Cho, Hs., Kim, Th., Mohammed, S., Adeli, H., Oh, Mk., Lee, KW. (eds) Green and Smart Technology with Sensor Applications. ICTSM SIA GST 2011 2012 2012. Communications in Computer and Information Science, vol 338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35251-5_46
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DOI: https://doi.org/10.1007/978-3-642-35251-5_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35250-8
Online ISBN: 978-3-642-35251-5
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