Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A well-conditioned estimator for large-dimensional covariance matrices
Journal of Multivariate Analysis
Feature Discovery in Non-Metric Pairwise Data
The Journal of Machine Learning Research
Bilinear Discriminant Component Analysis
The Journal of Machine Learning Research
Brain-Computer Interfacing for Intelligent Systems
IEEE Intelligent Systems
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Machine-learning-based coadaptive calibration for brain-computer interfaces
Neural Computation
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Electroencephalographic signals are known to be nonstationary and easily affected by artifacts; therefore, their analysis requires methods that can deal with noise. In this work, we present a way to robustify the popular common spatial patterns CSP algorithm under a maxmin approach. In contrast to standard CSP that maximizes the variance ratio between two conditions based on a single estimate of the class covariance matrices, we propose to robustly compute spatial filters by maximizing the minimum variance ratio within a prefixed set of covariance matrices called the tolerance set. We show that this kind of maxmin optimization makes CSP robust to outliers and reduces its tendency to overfit. We also present a data-driven approach to construct a tolerance set that captures the variability of the covariance matrices over time and shows its ability to reduce the nonstationarity of the extracted features and significantly improve classification accuracy. We test the spatial filters derived with this approach and compare them to standard CSP and a state-of-the-art method on a real-world brain-computer interface BCI data set in which we expect substantial fluctuations caused by environmental differences. Finally we investigate the advantages and limitations of the maxmin approach with simulations.