To solve this problem, this paper develops a noise robustness low-rank learning (NRLRL) algorithm for EEG signal classification.  NRLRL establishes a low-rank subspace to connect the original data space and label space.  The asymmetric least squares support vector machine (aLS-SVM) is embedded into the objective function of NRLRL.  Several classification experiments with different noise intensity are designed on the Bonn data set, and the experiment results verify the effectiveness of the NRLRL algorithm.
NRLRL
The asymmetric least squares support vector machine (aLS-SVM) is embedded into the objective function of NRLRL.  The aLS-SVM finds the maximum quantile distance between the two classes of samples based on the pinball loss function, which further improves the noise robustness of the model. 
aLS-SVM