Lee Hyekyoung, Kim Yong-Deok, Cichocki Andrzej, Choi Seungjin
Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea.
Int J Neural Syst. 2007 Aug;17(4):305-17. doi: 10.1142/S0129065707001159.
In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.
在本文中，我们提出了一种用于连续脑电图分类的方法，其中我们采用非负张量分解（NTF）来确定判别性频谱特征，并使用维特比算法对多个心理任务进行连续分类。这是我们之前关于使用非负矩阵分解（NMF）进行脑电图分类工作的扩展。在脑机接口竞赛中对两个数据集进行的数值实验证实了该方法在连续脑电图分类中的有效性。
