Dynamic time series smoothing for symbolic interval data applied to neuroscience

Document Type

Journal Article

Publication Title

Information Sciences

Publisher

Elsevier

School

School of Medical and Health Sciences

RAS ID

30801

Comments

Nascimento, D. C., Pimentel, B., Souza, R., Leite, J. P., Edwards, D. J., Santos, T. E., & Louzada, F. (2020). Dynamic time series smoothing for symbolic interval data applied to neuroscience. Information Sciences, 517, 415-426. https://doi.org/10.1016/j.ins.2019.12.026

Abstract

This work aimed to appraise a multivariate time series, high-dimensionality data-set, presented as intervals using a Symbolic Data Analysis (SDA) approach. SDA reduces data dimensionality, considering the complexity of the model information through a set-valued (interval or multi-valued). Additionally, Dynamic Linear Models (DLM) are distinguished by modeling univariate or multivariate time series in the presence of non-stationarity, structural changes and irregular patterns. We considered neurophysiological (EEG) data associated with experimental manipulation of verticality perception in humans, using transcranial electrical stimulation. The innovation of the present work is centered on use of a dynamic linear model with SDA methodology, and SDA applications for analyzing EEG data.

DOI

10.1016/j.ins.2019.12.026

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