TY - CONF A1 - Fedotenkova, Mariia A1 - Beim Graben, Peter A1 - Sleigh, Jamie A1 - Hutt, Axel A2 - Rojas, Ignacio A2 - Pomares, Hector A2 - Valenzuela, Olga T1 - Time-frequency representations as phase space reconstruction in symbolic recurrence structure analysis T2 - Advances in time series analysis and forecasting, selected contributions from ITISE 2016 N2 - Recurrence structures in univariate time series are challenging to detect. We propose a combination of symbolic and recurrence analysis in order to identify recurrence domains in the signal. This method allows to obtain a symbolic representation of the data. Recurrence analysis produces valid results for multidimensional data, however, in the case of univariate time series one should perform phase space reconstruction first. In this chapter, we propose a new method of phase space reconstruction based on the signal's time-frequency representation and compare it to the delay embedding method. We argue that the proposed method outperforms the delay embedding reconstruction in the case of oscillatory signals. We also propose to use recurrence complexity as a quantitative feature of a signal. We evaluate our method on synthetic data and show its application to experimental EEG signals. Y1 - 2017 UR - https://opus4.kobv.de/opus4-UBICO/frontdoor/index/index/docId/20693 SN - 978-3-319-55789-2 SN - 978-3-319-55788-5 SP - 89 EP - 102 PB - Springer CY - Cham ER -