| Full text | |
| Author(s): |
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]
Total Authors: 7
|
| Affiliation: | [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
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | INFORMATION SCIENCES; v. 517, p. 415-426, MAY 2020. |
| Web of Science Citations: | 0 |
| 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. (C) 2019 Elsevier Inc. All rights reserved. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |