Title
EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS.
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.
Year
DOI
Venue
2017
10.1016/j.endm.2017.03.011
Electronic Notes in Discrete Mathematics
Keywords
Field
DocType
Electroencephalogram,Brain fingerprinting,Biometrics,Variable Neighborhood Search,Forecasting and Time series
Variable neighborhood search,Computer science,Sampling (signal processing),Fuzzy logic,Brain activity and meditation,Artificial intelligence,Biometrics,Machine learning,Electroencephalography,Brain fingerprinting,Metaheuristic
Journal
Volume
ISSN
Citations 
58
1571-0653
1
PageRank 
References 
Authors
0.63
2
15