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      • Department of Electrical and Electronics Engineering
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      Baseline regularized sparse spatial filters

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      Author
      Onaran, İbrahim
      Ince, N.F.
      Cetin, A. Enis
      Date
      2013
      Source Title
      2013 IEEE International Conference on Acoustics, Speech and Signal Processing
      Publisher
      IEEE
      Pages
      1133 - 1137
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      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 regularization
      Brain 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/11693/27970
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/ICASSP.2013.6637827
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      • Department of Electrical and Electronics Engineering 3524
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