Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6951261 | Biomedical Signal Processing and Control | 2016 | 6 Pages |
Abstract
Robust and sparse modeling are two important issues in brain-computer interface systems. L1-norm-based common spatial patterns (CSP-L1) method is a recently developed technique that seeks robust spatial filters by using L1-norm-based dispersions. However, the spatial filters obtained are still dense, and thus lack interpretability. This paper presents a regularized version of CSP-L1 with sparsity, termed as sp-CSPL1. It produces sparse spatial filters, which eliminate redundant channels and retain meaningful EEG signals. The sparsity is induced by penalizing the objective function of CSP-L1 with the L1-norm. The sp-CSPL1 approach uses the L1-norm twice for inducing sparsity and defining dispersions simultaneously. The presented sp-CSPL1 algorithm is evaluated on two publicly available EEG data sets, on which it shows significant improvement in classification accuracy.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Xiaomeng Li, Xuesong Lu, Haixian Wang,