Title :
Sparse common spatial patterns in brain computer interface applications
Author :
Goksu, Fikri ; Ince, N. Firat ; Tewfik, Ahmed H.
Author_Institution :
Electr. & Comput. Eng. Dept., Univ. of Minnesota, Twin Cities, MN, USA
Abstract :
The Common Spatial Pattern (CSP) method is a powerful technique for feature extraction from multichannel neural activity and widely used in brain computer interface (BCI) applications. By linearly combining signals from all channels, it maximizes variance for one condition while minimizing for the other. However, the method overfits the data in presence of dense recordings and limited amount of training data. To overcome this problem we construct a sparse CSP (sCSP) method such that only subset of channels contributes to feature extraction. The sparsity is achieved by a greedy search based generalized eigenvalue decomposition approach with low computational complexity. Our contributions in this study are extension of the greedy search based solution to have multiple sparse filters and its application in a BCI problem. We show that sCSP outperforms traditional CSP in the classification challenge of the multichannel ECoG data set of BCI competition 2005. Furthermore, it achieves nearly similar performance as infeasible exhaustive search and better than that of obtained by LI norm based sparse solution.
Keywords :
brain-computer interfaces; computational complexity; eigenvalues and eigenfunctions; feature extraction; filtering theory; greedy algorithms; medical signal processing; signal classification; BCI application; L1 norm based sparse solution; brain computer interface application; computational complexity; eigenvalue decomposition approach; feature extraction; greedy search; multichannel neural activity; sCSP method; sparse CSP method; sparse common spatial pattern method; sparse filter; Covariance matrix; Eigenvalues and eigenfunctions; Electroencephalography; Feature extraction; Optimization; Search methods; Spatial filters; Common Spatial Patterns; Generalized Eigenvalue Decomposition; Greedy Search; Sparsity;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5946458