An iterative self-training support vector machine algorithm in brain-computer interfaces

Abstract

In this paper, an iterative self-training Support Vector Machine (SVM) algorithm combined feature re-extraction is proposed for semi-supervised learning, which only needs a small set of labeled samples to train classifier and is thus very useful in Brain-Computer Interface (BCI) design. Two methods, the model selection based self-training and the confidence criterion, respectively, is also proposed for searching the best parameter pair of SVM and selecting the most useful unlabeled data to expand the labeled training data set. The Dataset IVa of BCI Competition III, is presented to demonstrate the validity of our algorithm with statistical significance test. As an iterative algorithm, experimental results of the proposed algorithm show the validity of re-extracting feature and the robustness of the feature to the noise. In addition, the convergence of the proposed algorithm and the validity of the method measuring the consistency of the feature are also demonstrated in experiments.