Transductive SVM for reducing the training effort in BCI

A brain-computer interface (BCI) provides a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In this work, the main concern is to reduce the training effort for BCI, which is often tedious and time consuming. Here we introduce a transductive support vector machines (TSVM) algorithm for the classification of EEG signals associated with mental tasks. TSVM possess the property of using both labeled and unlabeled data for reducing the calibration time in BCI and achieving good performance in classification accuracy. The advantages of the proposed method over the traditional supervised support vector machines (SVM) method are confirmed by about 2%-9% higher classification accuracies on a set of EEG recordings of three subjects from three-tasks-based mental imagery experiments.