  The common spatial pattern analysis (CSP) is a widely used signal processing
technique in brain-computer interface (BCI) systems to increase the
signal-to-noise ratio in electroencephalogram (EEG) recordings. Despite its
popularity, the CSP's performance is often hindered by the nonstationarity and
artifacts in EEG signals. The minmax CSP improves the robustness of the CSP by
using data-driven covariance matrices to accommodate the uncertainties. We show
that by utilizing the optimality conditions, the minmax CSP can be recast as an
eigenvector-dependent nonlinear eigenvalue problem (NEPv). We introduce a
self-consistent field (SCF) iteration with line search that solves the NEPv of
the minmax CSP. Local quadratic convergence of the SCF for solving the NEPv is
illustrated using synthetic datasets. More importantly, experiments with
real-world EEG datasets show the improved motor imagery classification rates
and shorter running time of the proposed SCF-based solver compared to the
existing algorithm for the minmax CSP.
