A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system

Yuanqing Li, Cuntai Guan, Huiqi Li, Zhengyang Chin

Abstract: In this paper, we first present a self-training semi-supervised support vector machine (SVM) algorithm and its corresponding model selection method, which are designed to train a classifier with small training data. Next, we prove the convergence of this algorithm. Two examples are presented to demonstrate the validity of our algorithm with model selection. Finally, we apply our algorithm to a data set collected from a P300-based brain computer interface (BCI) speller. This algorithm is shown to be able to significantly reduce training effort of the P300-based BCI speller.