Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6637827 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
approximation theory,electrodes,feature extraction,medical signal processing,minimisation,spatial filters,BMI systems,CSP method,ECoG data,RQ,Rayleigh quotient,SSP method,approximated ℓ1 penalty,baseline regularization,baseline regularized sparse spatial filters,brain machine interfaces,common spatial pattern method,feature extraction,finger movements,linear spatial projections,multichannel electrode placement,multichannel neural activity,objective function,sparse channels,spatially sparse projection method,unconstrained minimization,Baseline regularization,Brain machine interfaces,Common spatial patterns,Sparse spatial projections,Unconstrained optimization | Common spatial pattern,Rayleigh quotient,Mathematical optimization,Pattern recognition,Computer science,Approximation theory,Communication channel,Feature extraction,Minimisation (psychology),Minification,Regularization (mathematics),Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ibrahim Onaran | 1 | 24 | 6.52 |
| Nuri Firat Ince | 2 | 13 | 4.09 |
| A. Enis Çetin | 3 | 871 | 118.56 |