The effect of methods addressing the class imbalance problem on P300 detection

This paper studies empirically the effect of different sampling methods on training classifiers on the imbalanced data of the BCI P300 Speller. Both over-sampling and under-sampling are considered. Besides some existing methods like SMOTE that have been shown to be effective in addressing the class imbalance problem we also proposed a new under-sampling technology, namely, instance-remove algorithm which is based on the property of P300 data sets. The classifiers for testing are FLDA and linear SVM. Experimental results suggest that not all of the sampling methods are effective in P300 detection, and even the same method may have different influence on different classifiers. It reveals that the SMOTE technique which is a variant of over-sampling is very effective in training an FLDA classifier while other methods are slightly effective or ineffective both in training FLDA and Linear SVM. The study also suggests that the over-sampling is more effective than under-sampling on both classifiers.