EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS.
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Data
2017
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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.
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Palavras-chave
Electroencephalogram, Neighborhood search, Biometrics, Variable
Citação
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.