title: A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces.
abstract: Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which channels and frequency band are key parameters. First, a semi-supervised support vector machine algorithm is proposed for automatically selecting a set of channels with given frequency band. Next, this algorithm is extended for joint channel-frequency selection. In this approach, both training data with labels and test data without labels are used for training a classifier. Hence it can be used in small training data case. Finally, our algorithms are applied to a BCI competition data set. Our data analysis results show that these algorithms are effective for selection of frequency band and channels when the training data set is small.
authors: The College of Automation Science and Engineering, South China University of Technology, 510640 Guangzhou, China.
authors: Jinyi
authors: J
authors: Long
authors: Yuanqing
authors: Y
authors: Li
authors: Zhuliang
authors: Z
authors: Yu