Dynamic time series smoothing for symbolic interval data applied to neuroscience

Authors/Creators

Diego C. Nascimento
Bruno Pimentel
Renata Souza
João P. Leite
Dylan J. Edwards, Edith Cowan UniversityFollow
Taiza E. G. Santos
Francisco Louzada

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.

RAS ID

30801

Document Type

Journal Article

Date of Publication

1-1-2020

School

School of Medical and Health Sciences

Copyright

subscription content

Publisher

Elsevier

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

Recommended Citation

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. DOI: https://doi.org/10.1016/j.ins.2019.12.026