Dynamic time series smoothing for symbolic interval data applied to neuroscience

被引：7
|
作者：
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 ]
机构：
[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] Moss Rehabil Res Inst, Elkins Pk, PA USA
[5] Edith Cowan Univ, Sch Med & Hlth Sci, Joondalup, WA, Australia
来源：
INFORMATION SCIENCES | 2020年 / 517卷
基金：
巴西圣保罗研究基金会;
关键词：
State space model; Symbolic data analysis; Verticality perception; LINEAR-REGRESSION; ROBUST REGRESSION; MODELS;
D O I：
10.1016/j.ins.2019.12.026
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：415 / 426
页数：12
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