Cluster impurity and forward-backward error maximization-based active learning for EEG signals classification

This paper investigates how to apply active learning for the classification of motor imagery electroencephalography (EEG) signals to boost the performance for small training size. A new criterion is proposed to select the most representative and informative queries. The candidates are firstly chosen from the samples close to the center of the cluster that has the highest impurity of classes. A predefined number of such candidates and classifiers are forwardly buffered. Subsequently, the query is chosen such that the buffered classifiers can backward maximize the classification errors on labeled data. Experimental results conducted on the BCI competition IV data set IVb show the superior performance of the proposed active learning scheme, which is on average 5.12% higher in accuracy than that of the passive method by choosing the training size from 28 to 112.