Abstract
A method is proposed to determine the similarity of a collection of time series. As a first step, one extracts events from the time series, in other words, one converts each time series into a point process (“event sequence”); next one tries to align the events from those different point processes. The better the events can be aligned, the more similar the original time series are considered to be. The proposed method is applied to predict mild cognitive impairment (MCI) from EEG and to investigate the dynamics of oscillatory-event synchrony of steady-state visually evoked potentials (SSVEP).
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Dauwels, J., Vialatte, F., Weber, T., Cichocki, A. (2009). An Exemplar-Based Statistical Model for the Dynamics of Neural Synchrony. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_39
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DOI: https://doi.org/10.1007/978-3-642-02490-0_39
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