Baseline regularized sparse spatial filters

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dc.citation.epage1137en_US
dc.citation.spage1133en_US
dc.contributor.authorOnaran, İbrahimen_US
dc.contributor.authorInce, N.F.en_US
dc.contributor.authorCetin, A. Enisen_US
dc.coverage.spatialVancouver, BC, Canadaen_US
dc.date.accessioned2016-02-08T12:07:01Z
dc.date.available2016-02-08T12:07:01Z
dc.date.issued2013en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 26-31 May 2013en_US
dc.description.abstractThe common spatial pattern (CSP) method has large number of applications in brain machine interfaces (BMI) to extract features from the multichannel neural activity through a set of linear spatial projections. These spatial projections minimize the Rayleigh quotient (RQ) as the objective function, which is the variance ratio of the classes. 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 to some adequate number. We introduce a spatially sparse projection (SSP) method that renders unconstrained minimization possible via a new objective function with an approximated ℓ1 penalty. We apply our new algorithm with a baseline regularization to the ECoG data involving finger movements to gain stability with respect to the number of sparse channels. © 2013 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:07:01Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2013en
dc.identifier.doi10.1109/ICASSP.2013.6637827en_US
dc.identifier.urihttp://hdl.handle.net/11693/27970
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICASSP.2013.6637827en_US
dc.source.title2013 IEEE International Conference on Acoustics, Speech and Signal Processingen_US
dc.subjectBaseline regularizationen_US
dc.subjectBrain machine interfacesen_US
dc.subjectCommon spatial patternsen_US
dc.subjectSparse spatial projectionsen_US
dc.subjectUnconstrained optimizationen_US
dc.subjectBaseline regularizationen_US
dc.subjectBrain machine interfaceen_US
dc.subjectCommon spatial patternsen_US
dc.subjectSparse spatial projectionsen_US
dc.subjectUnconstrained optimizationen_US
dc.subjectNeuronsen_US
dc.subjectSignal processingen_US
dc.titleBaseline regularized sparse spatial filtersen_US
dc.typeConference Paperen_US

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