A semi-supervised SVM learning algorithm for joint feature extraction and classification in brain computer interfaces

In machine learning based Brain Computer Interfaces (BCIs), it is a challenge to use only a small amount of labelled data to build a classifier for a specific subject. This challenge was specifically addressed in BCI Competition 2005 [3]. Moreover, an effective BCI system should be adaptive to tackle the dynamic variations in brain signal. One of the solutions is to have its parameters adjustable while the system is used online. In this paper we introduce a new semi-supervised support vector machine (SVM) learning algorithm. In this method, the feature extraction and classification are jointly performed in iterations. This method allows us to use a small training set to train the classifier while maintaining high performance. Therefore, the tedious initial calibration process is shortened. This algorithm can be used online to make the BCI svstem robust to possible signal changes. We analyze two important issues of the proposed algorithm, the robustness of the features to noise and the convergence of algorithm. We applied our method to data from BCI competition 2005, and the results demonstrated the validity of the proposed algorithm.