Solving the data imbalance problem of P300 detection via Random Under-Sampling Bagging SVMs

Abstract

The imbalance problem exists in P300 EEG data sets because P300 potential are collected under the condition of Oddball experimental paradigm. Hence, a P300 detection method, namely RUSBagging SVMs, is proposed in this paper to solve the imbalance problem and make an improvement. This algorithm re-samples the data sets at first to generate a rebalanced training set in one round of iteration and trains an SVM classifier based on the training set. Next, the SVM classifiers are integrated to make a final decision. In the integration of several classifiers, the information that is lost in the under-sampling process is generally considered. Therefore, the method is relatively robust. The experiments of character recognition based on P300 EEG data signals are conducted to examine the method. It is concluded from the experiments that RUSBagging method can indeed improve the performance of P300 detection by solving the imbalance problem in EEG data sets.