Advanced SSVEP stimulator for brain-computer interface and signal classification with using convolutional neural network

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dc.authoridORALHAN, Zeki/0000-0003-2841-6115
dc.contributor.authorOralhan, Z.
dc.date.accessioned2025-02-24T17:19:06Z
dc.date.available2025-02-24T17:19:06Z
dc.date.issued2019
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractSteady-state visual evoked potential, type of electroencephalography (EEG) signal, that is used for brain-computer interface systems are considered in this Letter. Steady-state visual evoked potential stimulator is needed for realising the signal on the scalp. Besides, information transfer rate is the most significant parameter to evaluate overall performance of a brain-computer interface. EEG signal classification methods, task completion time, and signal stimulator structure affect information transfer rate values. In this Letter, the authors aimed to reach a high information transfer rate by using the proposed signal stimulator and classification method that has new architectures. Eight flickering objects that provide 36 different characters to spell were used. This stimuli optimisation prevented the effect of eye fatigue on signal. Therefore, steady-state visual evoked potential was elicited dominantly. Moreover, 1D convolutional neural network for signal classification was proposed in this Letter. Online experimental data was also classified with canonical correlation analysis that is most commonly used in brain-computer interface systems. The authors compared results according to both of the classification methods. They have reached average value of information transfer rate as 50.67 bit/min with the proposed classification method. This result is significantly higher than similar studies.
dc.identifier.doi10.1049/el.2019.2579
dc.identifier.endpage1330
dc.identifier.issn0013-5194
dc.identifier.issn1350-911X
dc.identifier.issue25
dc.identifier.scopus2-s2.0-85076558235
dc.identifier.scopusqualityQ3
dc.identifier.startpage1329
dc.identifier.urihttps://doi.org/10.1049/el.2019.2579
dc.identifier.urihttps://hdl.handle.net/20.500.14440/995
dc.identifier.volume55
dc.identifier.wosWOS:000517832500007
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorOralhan, Z.
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofElectronics Letters
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250201
dc.subjectneural nets
dc.subjectelectroencephalography
dc.subjectmedical signal processing
dc.subjectbrain-computer interfaces
dc.subjectvisual evoked potentials
dc.subjectsignal classification
dc.subjectconvolutional neural network
dc.subjectbrain-computer interface systems
dc.subjectclassification method
dc.subjectadvanced SSVEP stimulator
dc.subjectsteady-state visual evoked potential stimulator
dc.subjectEEG signal classification methods
dc.subjecttask completion time
dc.subjectsignal stimulator structure
dc.subjectinformation transfer rate values
dc.subjecthigh information transfer rate
dc.titleAdvanced SSVEP stimulator for brain-computer interface and signal classification with using convolutional neural network
dc.typeEditorial

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