dc.contributor.author | Onaran, I. | en_US |
dc.contributor.author | Ince, N. F. | en_US |
dc.contributor.author | Çetin, A. Enis | en_US |
dc.date.accessioned | 2015-07-28T12:05:28Z | |
dc.date.available | 2015-07-28T12:05:28Z | |
dc.date.issued | 2013-05 | en_US |
dc.identifier.issn | 1746-8094 | |
dc.identifier.uri | http://hdl.handle.net/11693/13276 | |
dc.description.abstract | Common 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. 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 itis 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 exploits the unconstrained minimization of a new objective function with approximated 1
penalty. Unlike the RQ, this new objective function depends on the magnitude of the sparse filter. The
SSP method is employed to classify the multiclass ECoG and two class EEG data sets. We compared our
results with a recently introduced sparse CSP solution based on 0 norm. Our method outperforms the
standard CSP method and provides comparable results to 0 norm based solution and it is associated
with less computational complexity. We also conducted several simulation studies on the effect of noisy
channel and intersession variability on the performance of the CSP and sparse filters. | en_US |
dc.language.iso | English | en_US |
dc.source.title | Biomedical Signal Processing and Control | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1016/j.bspc.2012.10.003 | en_US |
dc.subject | Brain Machine Interfaces | en_US |
dc.subject | Common Spatial Patterns | en_US |
dc.subject | Sparse Spatial Projections | en_US |
dc.subject | Rayleigh Quotient | en_US |
dc.subject | Unconstrained Optimization | en_US |
dc.title | Sparse spatial filter via a novel objective function minimization with smooth ℓ1 regularization | en_US |
dc.type | Article | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.citation.spage | 282 | en_US |
dc.citation.epage | 288 | en_US |
dc.citation.volumeNumber | 8 | en_US |
dc.citation.issueNumber | 3 | en_US |
dc.identifier.doi | 10.1016/j.bspc.2012.10.003 | en_US |
dc.publisher | Elsevier | en_US |
dc.contributor.bilkentauthor | Çetin, A. Enis | |
buir.contributor.orcid | Çetin, A. Enis|0000-0002-3449-1958 | en_US |