Detection of P300 based on Artficial Bee Colony

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dc.authorscopusid57189326208
dc.authorscopusid24512252600
dc.contributor.authorAytekin, S.A.
dc.contributor.authorKiyan, T.
dc.date.accessioned2022-05-11T14:16:02Z
dc.date.available2022-05-11T14:16:02Z
dc.date.issued2016
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü
dc.descriptionInstitute for Systems and Technologies of Information, Control and Communication (INSTICC)
dc.description9th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2016 - Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016 -- 21 February 2016 through 23 February 2016 -- -- 120093
dc.description.abstractA Brain-Computer Interface (BCI) is a system that allows users to communicate with their environment through cerebral activity. P300 signal, which is used widely in BCI applications, is produced as a response to a stimulus and can be measured in the parietal lobe of the brain. In this paper, an approach which is a swarm intelligence technique, called Artificial Bee Colony (ABC) together with Multilayer Perceptron (MLP) is used for the detection of P300 signals to achieve high accuracy. The system is based on the P300 evoked potential and is tested on four healthy subjects. It has two main blocks, feature extraction and classification. In the feature extraction block, Power Spectrum Density (PSD) is used whereas ABC was employed to train Multi Layer Perceptron (MLP) in the classification part. This method is compared to other methods such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). The best result that is achieved in this work is 99.8%. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
dc.identifier.doi10.5220/0005696001830189
dc.identifier.endpage189
dc.identifier.isbn978-9897581700
dc.identifier.scopus2-s2.0-84969256438
dc.identifier.startpage183
dc.identifier.urihttps://doi.org/10.5220/0005696001830189
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6158
dc.indekslendigikaynakScopus
dc.institutionauthorAytekin, S.A.
dc.language.isoen
dc.publisherSciTePress
dc.relation.ispartofBIOSIGNALS 2016 - 9th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Bee Colony
dc.subjectBrain-Computer Interface
dc.subjectP300
dc.subjectBiomedical engineering
dc.subjectBiomedical signal processing
dc.subjectBiomimetics
dc.subjectBrain
dc.subjectDiscriminant analysis
dc.subjectExtraction
dc.subjectFeature extraction
dc.subjectOptimization
dc.subjectSupport vector machines
dc.subjectArtificial bee colonies
dc.subjectArtificial bee colonies (ABC)
dc.subjectFeature extraction and classification
dc.subjectLinear discriminant analysis
dc.subjectMulti layer perceptron
dc.subjectP300
dc.subjectPower spectrum density
dc.subjectSwarm intelligence techniques
dc.subjectBrain computer interface
dc.titleDetection of P300 based on Artficial Bee Colony
dc.typeConference Object

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