title: Robust common spatial filters with a maxmin approach.
abstract: Electroencephalographic signals are known to be non-stationary and easily affected by artifacts, therefore their analysis requires methods that can deal with noise. In this work we present two ways of calculating robust common spatial patterns under a maxmin approach. The worst-case objective function is optimized within prefixed sets of the covariance matrices that are defined either very simply as identity matrices or in a data driven way using PCA. We test common spatial filters derived with these two approaches with real world brain-computer interface (BCI) data sets in which we expect substantial "day-to-day" fluctuations (session transfer problem). We compare our results with the classical common spatial filters and show that both can improve the performance of the latter.
authors: IDA Group at FIRST.Fraunhofer, Kekulestr. 7, Berlin 12489, Germany.
authors: Motoaki
authors: M
authors: Kawanabe
authors: Carmen
authors: C
authors: Vidaurre
authors: Simon
authors: S
authors: Scholler
authors: Klaus-Robert
authors: KR
authors: Müller
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001698:Biomedical Engineering / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D012815:Signal Processing, Computer-Assisted