Dynamic time series smoothing for symbolic interval data applied to neuroscience

Autor(es)

Nascimento, Diego C. ^[1] ; Pimentel, Bruno ^[1] ; Souza, Renata ^[2] ; Leite, Joao P. ^[3] ; Edwards, Dylan J. ^{[4, 5]} ; Santos, Taiza E. G. ^[3] ; Louzada, Francisco ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math Sci & Comp, Sao Carlos - Brazil

^[2] Univ Fed Pernambuco, Ctr Informat, Recife, PE - Brazil

^[3] Univ Sao Paulo, Ribeirao Preto Med Sch, Ribeirao Preto - Brazil

^[4] Edith Cowan Univ, Sch Med & Hlth Sci, Joondalup, WA - Australia

^[5] Moss Rehabil Res Inst, Elkins Pk, PA - USA

• Tipo de documento: Artigo Científico
• Fonte: INFORMATION SCIENCES; v. 517, p. 415-426, MAY 2020.
• Citações Web of Science: 0

Resumo

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. (C) 2019 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: José Alberto Cuminato
• Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs