Iterative clustering and support vectors-based high-confidence query selection for motor imagery EEG signals classification

This paper proposes a novel active learning method for the classification of motor imagery electroencephalogram (EEG) signals. Specifically, we propose an iterative clustering and support vector-based criterion to select samples of high-confidence to construct a robust training set. The common spatial pattern (CSP)-based features are iteratively clustered till the number of support vectors in the cluster is less than a predefined threshold. A predefined number of samples close to the cluster centers are chosen. When such clusters cannot be found, the samples that are of farthest distances to a group of support vectors of class “0” and “1” are alternately chosen. Experimental results on BCI competition IV dataset IIb show superior performance compared with a baseline method, which is 9% increase in accuracy averaged across subjects and training sizes.