TY - GEN
T1 - A maxmin approach to optimize spatial filters for eeg single-trial classification
AU - Kawanabe, Motoaki
AU - Vidaurre, Carmen
AU - Blankertz, Benjamin
AU - Müller, Klaus Robert
PY - 2009
Y1 - 2009
N2 - Electroencephalographic single-trial analysis requires methods that are robust with respect to noise, artifacts and non-stationarity among other problems. This work contributes by developing a maxmin approach to robustify the common spatial patterns (CSP) algorithm. By optimizing the worst-case objective function within a prefixed set of the covariance matrices, we can transform the respective complex mathematical program into a simple generalized eigenvalue problem and thus obtain robust spatial filters very efficiently. We test our maxmin CSP method with real world brain-computer interface (BCI) data sets in which we expect substantial fluctuations caused by day-to-day or paradigm-to-paradigm variability or different forms of stimuli. The results clearly show that the proposed method significantly improves the classical CSP approach in multiple BCI scenarios.
AB - Electroencephalographic single-trial analysis requires methods that are robust with respect to noise, artifacts and non-stationarity among other problems. This work contributes by developing a maxmin approach to robustify the common spatial patterns (CSP) algorithm. By optimizing the worst-case objective function within a prefixed set of the covariance matrices, we can transform the respective complex mathematical program into a simple generalized eigenvalue problem and thus obtain robust spatial filters very efficiently. We test our maxmin CSP method with real world brain-computer interface (BCI) data sets in which we expect substantial fluctuations caused by day-to-day or paradigm-to-paradigm variability or different forms of stimuli. The results clearly show that the proposed method significantly improves the classical CSP approach in multiple BCI scenarios.
UR - http://www.scopus.com/inward/record.url?scp=68749091411&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02478-8_84
DO - 10.1007/978-3-642-02478-8_84
M3 - Conference contribution
AN - SCOPUS:68749091411
SN - 3642024777
SN - 9783642024771
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 674
EP - 682
BT - Bio-Inspired Systems
T2 - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009
Y2 - 10 June 2009 through 12 June 2009
ER -