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Título: | EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS. |
Autor(es): | 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, Mateus Nazario Netto, Guilherme Gaigher Pinto, Alysson Alves Elias, Marcelo Eustaquio Versiani Gonçalves Filho, Dalton Cesar de Oliveira Oliveira, Thays Aparecida de |
Palavras-chave: | Electroencephalogram Neighborhood search Biometrics Variable |
Data do documento: | 2017 |
Referência: | 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. |
Resumo: | 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 |
Link para o artigo: | https://www.sciencedirect.com/science/article/pii/S1571065317300471 |
DOI: | https://doi.org/10.1016/j.endm.2017.03.011 |
ISSN: | 1571-0653 |
Aparece nas coleções: | DECOM - Artigos publicados em periódicos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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ARTIGO_EEGTimeSeries.pdf Restricted Access | 464,09 kB | Adobe PDF | Visualizar/Abrir |
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