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primary topic: EEG Motor Imagery Signal Classification Using Firefly Support Vector Machine

EEG Motor Imagery Signal Classification Using Firefly Support Vector Machine
type: AcademicArticle
type: Article
abstract: This Study proposed Firefly-Support vector machine (FASVM) method to improve the accuracy of EEG motor imagery signals classification. Redundancy of features in EEG signal classification can affect its performance. Firefly is a metaheuristic optimization based on firefly behavior. Firefly algorithm is used to select optimal subset of features in order to increase the classification accuracy. Common Spatial Pattern (CSP) is used to extract EEG signals features before selected by firefly algorithm. The purpose of CSP is to maximize the variance for one class and to minimize the other class variance. This study used BCI Competition III data set IVa. Feature vector extracted from CSP is selected using FASVM. The accuracy of SVM is used as objective function for firefly algorithm optimisation. The proposed method FASVM produced good result with average accuracy of 93.20 . © 2018 IEEE.
date: 2018
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Institute of Electrical and Electronics Engineers Inc.
type: Organization

International Conference on Intelligent and Advanced System, ICIAS 2018
type: Collection

B.A. Pramudita
type: Person
given name: B.A.
family name: Pramudita

L.I. Izhar
type: Person
given name: L.I.
family name: Izhar

S. Wibirama
type: Person
given name: S.
family name: Wibirama

Y.D.H. Nugroho
type: Person
given name: Y.D.H.
family name: Nugroho

N.A. Setiawan
type: Person
given name: N.A.
family name: Setiawan

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primary topic: EEG Motor Imagery Signal Classification Using Firefly Support Vector Machine