A novel objective function minimization for sparse spatial filters

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage191en_US
dc.citation.spage188en_US
dc.contributor.authorOnaran, İ.en_US
dc.contributor.authorİnce, N. F.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialTrabzon, Turkeyen_US
dc.date.accessioned2016-02-08T11:54:10Z
dc.date.available2016-02-08T11:54:10Z
dc.date.issued2014en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 23-25 April 2014en_US
dc.descriptionConference Name: 22nd Signal Processing and Communications Applications Conference, SIU 2014en_US
dc.description.abstractCommon spatial pattern (CSP) method is widely used in brain machine interface (BMI) applications to extract features from the multichannel neural activity through a set of spatial projections. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited. We introduce a spatially sparse projection (SSP) method that exploits the unconstrained minimization of a new objective function with approximated l\ penalty. The SSP method is employed to classify the two class EEG data set. Our method outperforms the standard CSP method and provides comparable results to £o norm based solution and it is associated with less computational complexity.en_US
dc.identifier.doi10.1109/SIU.2014.6830197en_US
dc.identifier.urihttp://hdl.handle.net/11693/27463
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2014.6830197en_US
dc.source.titleProceedings of the 22nd Signal Processing and Communications Applications Conference, SIU 2014en_US
dc.subjectNeuronsen_US
dc.subjectOptimizationen_US
dc.subjectBrain machine interfaceen_US
dc.subjectCommon spatial patternsen_US
dc.subjectRayleigh quotientsen_US
dc.subjectSparse Spatial Projectionsen_US
dc.subjectUnconstrained optimizationen_US
dc.subjectSignal processingen_US
dc.titleA novel objective function minimization for sparse spatial filtersen_US
dc.typeConference Paperen_US
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