Classification of Mental Task EEG Records Using Hjorth Parameters

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dc.contributor.authorTurk, Omer
dc.contributor.authorSeker, Mesut
dc.contributor.authorAkpolat, Veysi
dc.contributor.authorOzerdem, Mchmet Sirac
dc.date.accessioned14.07.201910:50:10
dc.date.accessioned2019-07-16T20:43:58Z
dc.date.available14.07.201910:50:10
dc.date.available2019-07-16T20:43:58Z
dc.date.issued2017
dc.department[Belirlenecek]en_US
dc.description25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYen_US
dc.description.abstractThe effects of mental activities on brain dynamics is the main field that studied for a long time, but the results of studies have not reached the desired level. The aim of present study was to classify the mental task EEG records by using Hjorth parameters. hi this study, EEG signals that recorded from 9 subjects were used. EEG signals were recorded by applying a experimental paradigm which contains five stimuli related to different mental task. These stimuli are defined as condition word mental subtraction spatial navigation right hand motor imagery and feet motor imagery Wavelet packet transform was used to obtain sub bands of EEC signals. Statistical parameters that consist of mobility, complexity and Mahalanobis distance were applied to sub-bands. Feature vectors were classified by using artificial neural network. When classification performances related to mental activities were examined, the best classification accuracy was obtained as nearly 80% for 'condition word - mental subtraction', ('spatial navigation feet motor imagery;' and 'spatial navigation - condition word'. The lowest classification accuracy was obtained for 'mental subtraction - right hand motor imagery,', 'condition word - right hand motor imagery' and 'spatial navigation right hand motor imagery'. The classification accuracies related to all stimuli that classifed among themselves were obtained as 77,61%.en_US
dc.description.sponsorshipTurk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Univen_US
dc.identifier.isbn978-1-5090-6494-6
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.12514/1314
dc.identifier.wosWOS:000413813100471en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)en_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWaveler Packet Decompositionen_US
dc.subjectHjorth Parametersen_US
dc.subjectMental Tasken_US
dc.titleClassification of Mental Task EEG Records Using Hjorth Parametersen_US
dc.typeConference Objecten_US

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