Baseline regularized sparse spatial filters
Author
Onaran, İbrahim
Ince, N.F.
Cetin, A. Enis
Date
2013Source Title
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Publisher
IEEE
Pages
1133 - 1137
Language
English
Type
Conference PaperItem Usage Stats
139
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views
115
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downloads
Abstract
The 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.
Keywords
Baseline regularizationBrain machine interfaces
Common spatial patterns
Sparse spatial projections
Unconstrained optimization
Baseline regularization
Brain machine interface
Common spatial patterns
Sparse spatial projections
Unconstrained optimization
Neurons
Signal processing
Permalink
http://hdl.handle.net/11693/27970Published Version (Please cite this version)
http://dx.doi.org/10.1109/ICASSP.2013.6637827Collections
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