title: Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion.
abstract: In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed for discriminant purpose. The basic idea of dCSP is to construct a Fisher-like criterion that extracts both between-class and within-class discriminant information. The classical CSP only considers separating class means, i.e., between-class scatter, as well as possible. In contrast, dCSP aims to maximize between-class scatter and meanwhile minimize within-class scatter. Computationally, dCSP is formulated as a generalized eigenvalue problem. Experiments on real EEG classification show the effectiveness of the proposed method.
authors: Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, 210096 Jiangsu, China. hxwang@seu.edu.cn
authors: Haixian
authors: H
authors: Wang
mesh_terms: D000465:Algorithms; D004560:Electricity; D004562:Electrocardiography / Q000379:methods*; D056149:Epicardial Mapping / Q000379:methods; D006801:Humans; D008955:Models, Cardiovascular; D010496:Pericardium / Q000502:physiology; D012815:Signal Processing, Computer-Assisted