title: L1-norm-based common spatial patterns.
abstract: Common spatial patterns (CSP) is a commonly used method of spatial filtering for multichannel electroencephalogram (EEG) signals. The formulation of the CSP criterion is based on variance using L2-norm, which implies that CSP is sensitive to outliers. In this paper, we propose a robust version of CSP, called CSP-L1, by maximizing the ratio of filtered dispersion of one class to the other class, both of which are formulated by using L1-norm rather than L2-norm. The spatial filters of CSP-L1 are obtained by introducing an iterative algorithm, which is easy to implement and is theoretically justified. CSP-L1 is robust to outliers. Experiment results on a toy example and datasets of BCI competitions demonstrate the efficacy of the proposed method.
authors.affiliation: Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, China. hxwang@seu.edu.cn
authors.forename: Haixian
authors.initials: H
authors.lastname: Wang
authors.forename: Qin
authors.initials: Q
authors.lastname: Tang
authors.forename: Wenming
authors.initials: W
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted