Affiliations: State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, China
Correspondence:
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Corresponding author: Xuemin Tan, State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China. E-mail:[email protected]
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.
Keywords: Self-training, support vector machine (SVM), brain-computer interface (BCI), model selection, confidence criterion
