关键词: 脑机接口, 脑磁图, 半监督, 模糊聚类

Abstract: The Magneto-Encephalo-Graphy (MEG) can be used as an input signal for Brain Computer Interface (BCI), which contains the pattern information of the hand movement direction. In view of the fact that the semi-supervised clustering combines the advantages of training data prior knowledge, a semisupervies fuzzy clustering algorithm based on training center was put forward. The algorithm was divided into lower-dimensional and improved semisupervised clustering. Principal component analysis and linear discriminant analysis were used to reduce the data from high-dimension to low-dimension. Improved semisupervised clustering based on fuzzy clustering for the training data added the training center in proportion to the test data center. The experimental results show that the average recognition rate of the proposed algorithm reaches to 55.1%, higher than that of the winner of the 2008 competition Ⅳ.

Key words: Brain Computer Interface (BCI), Magneto-Encephalo-Graphy (MEG), semi-supervised, fuzzy clustering