Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9586
Title: EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS.
Authors: Coelho, Vitor Nazário
Coelho, Igor Machado
Coelho, Bruno Nazário
Souza, Marcone Jamilson Freitas
Guimarães, Frederico Gadelha
Luz, Eduardo José da Silva
Barbosa, Alexandre Costa
Coelho, M. N.
Netto, Guilherme Gaigher
Pinto, Alysson Alves
Elias, M. E. V.
G. Filho, D. C. O.
Oliveira, Thays Aparecida de
Keywords: Electroencephalogram
Neighborhood search
Biometrics
Variable
Issue Date: 2017
Citation: COELHO, V. N. et al. EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS. Electronic Notes in Discrete Mathematics, v. 58, p. 79-86, 2017. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1571065317300471>. Acesso em: 16 jan. 2018.
Abstract: Brain activity can be seen as a time series, in particular, electroencephalogram (EEG) can measure it over a specific time period. In this regard, brain fingerprinting can be subjected to be learned by machine learning techniques. These models have been advocated as EEG-based biometric systems. In this study, we apply a recent Hybrid Focasting Model, which calibrates its if-then fuzzy rules with a hybrid GVNS metaheuristic algorithm, in order to learn those patterns. Due to the stochasticity of the VNS procedure, models with different characteristics can be generated for each individual. Some EEG recordings from 109 volunteers, measured using a 64-channels EEGs, with 160 HZ of sampling rate, are used as cases of study. Different forecasting models are calibrated with the GVNS and used for the classification purpose. New rules for classifying the individuals using forecasting models are introduced. Computational results indicate that the proposed strategy can be improved and embedded in the future biometric systems.
URI: http://www.repositorio.ufop.br/handle/123456789/9586
metadata.dc.identifier.uri2: https://www.sciencedirect.com/science/article/pii/S1571065317300471
ISSN: 15710653
Appears in Collections:DECOM - Artigos publicados em periódicos

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