Classification of event-related potentials using multivariate autoregressive modeling combined with simulated annealing | Zendy

Christos E. Vasios | Zendy; O.K. Matsopoulos | Zendy; Konstantina S. Nikita | Zendy; Nikolaos Uzunoglu | Zendy

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Classification of event-related potentials using multivariate autoregressive modeling combined with simulated annealing

Author(s) -

Christos E. Vasios,

O.K. Matsopoulos,

Konstantina S. Nikita,

Nikolaos Uzunoglu

Publication year - 2003

Publication title -

journal of automatic control

Language(s) - English

Resource type - Journals

eISSN - 2406-0984

pISSN - 1450-9903

DOI - 10.2298/jac0301007v

Subject(s) - computer science , autoregressive model , multivariate statistics , simulated annealing , artificial intelligence , feature selection , artificial neural network , feature extraction , pattern recognition (psychology) , data mining , machine learning , statistics , mathematics

In the present work, a new method for the classification of Event Related Potentials (ERPs) is proposed. The proposed method consists of two modules: the feature extraction module and the classification module. The feature extraction module comprises the implementation of the Multivariate Autoregressive model in conjunction with the Simulated Annealing technique, for the selection of optimum features from ERPs. The classification module is implemented with a single three-layer neural network, trained with the back-propagation algorithm and classifies the data into two classes: patients and control subjects. The method, in the form of a Decision Support System (DSS), has been thoroughly tested to a number of patient data (OCD, FES, depressives and drug users), resulting successful classification up to 100%

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