Abstract
The aim of this chapter is to identify stable points and stationary wavelets in EEG signals. Generally an EEG signal is a very complex nonstationary signal. It is very difficult to recognize specific EEG features such as Biometric patterns and Pathological changes. Using a repeated autocorrelation procedure and symmetry features of EEG time series on real EEG Time Series Data, we experimentally investigate stable points in EEG signals. Also we investigate standing waves shafts around these stable points, which reveals the existence of stationary wavelets in EEG signals.
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Stefanidis, V., Anogiannakis, G., Evangelou, A., Poulos, M. (2015). Stable EEG Features. In: Migdalas, A., Karakitsiou, A. (eds) Optimization, Control, and Applications in the Information Age. Springer Proceedings in Mathematics & Statistics, vol 130. Springer, Cham. https://doi.org/10.1007/978-3-319-18567-5_18
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DOI: https://doi.org/10.1007/978-3-319-18567-5_18
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