Robust common spatial filters with a maxmin approach.
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.
Kawanabe
Motoaki
M
IDA Group at FIRST.Fraunhofer, Kekulestr. 7, Berlin 12489, Germany.
Vidaurre
Carmen
C
Scholler
Simon
S
Müller
Klaus-Robert
KR
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