Self-training Algorithm for Channel Selection in P300-Based BCI Speller

摘要：

In this paper, we address the important problem of channel selection for a P300-based brain computer interface (BCI) speller system in the situation of insufficient training data with labels. An iterative semi-supervised support vector machine (SVM) is proposed for time segment selection as well as classification, in which both labeled training data and unlabeled test data are utilized. The performance of our algorithm has been evaluated through the analysis of a P300 dataset provided by BCI Competition 2005. The results show that our algorithm for channel selection and classification achieves satisfactory performance, meanwhile it can significantly reduce the training effort of the system1.

作者: Jinyi Long Zhenghui Gu Yuanqing Li Tianyou Yu

作者单位: College of Automation Science and Engineering, South China University of Technology, 510640

会议类型: 国际会议

会议名称: 2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)

会议地点: 西安

会议语种:英文

页码: 1-4

在线出版日期: 2011-10-18（万方平台首次上网日期，不代表论文的发表时间）