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dc.contributor.authorOnaran, İbrahimen_US
dc.contributor.authorInce, N.F.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.contributor.authorAbosch, A.en_US
dc.coverage.spatialCancun, Mexicoen_US
dc.date.accessioned2016-02-08T12:19:37Z
dc.date.available2016-02-08T12:19:37Z
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/11693/28401
dc.descriptionDate of Conference: 27 April-1 May 2011en_US
dc.description.abstractA hybrid state detection algorithm is presented for the estimation of baseline and movement states which can be used to trigger a free paced neuroprostethic. The hybrid model was constructed by fusing a multiclass Support Vector Machine (SVM) with a Hidden Markov Model (HMM), where the internal hidden state observation probabilities were represented by the discriminative output of the SVM. The proposed method was applied to the multichannel Electrocorticogram (ECoG) recordings of BCI competition IV to identify the baseline and movement states while subjects were executing individual finger movements. The results are compared to regular Gaussian Mixture Model (GMM)-based HMM with the same number of states as SVM-based HMM structure. Our results indicate that the proposed hybrid state estimation method out-performs the standard HMM-based solution in all subjects studied with higher latency. The average latency of the hybrid decoder was approximately 290ms. © 2011 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.title2011 5th International IEEE/EMBS Conference on Neural Engineeringen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/NER.2011.5910585en_US
dc.subjectElectrocorticogramen_US
dc.subjectFinger movementsen_US
dc.subjectGaussian Mixture Modelen_US
dc.subjectHidden stateen_US
dc.subjectHybrid decodersen_US
dc.subjectHybrid modelen_US
dc.subjectHybrid stateen_US
dc.subjectHybrid state estimationen_US
dc.subjectMulti-channelen_US
dc.subjectMulticlass support vector machinesen_US
dc.subjectNumber of stateen_US
dc.subjectState Detectionen_US
dc.subjectHidden Markov modelsen_US
dc.subjectElectrophysiologyen_US
dc.titleA hybrid SVM/HMM based system for the state detection of individual finger movements from multichannel ECoG signalsen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage457en_US
dc.citation.epage460en_US
dc.identifier.doi10.1109/NER.2011.5910585en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorÇetin, A. Enis


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