Nonnegative Tensor Factorization for Continuous EEG Classification
Nonnegative Tensor Factorization for Continuous EEG Classification
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DOI：
10.1142/s0129065707001159
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发表时间：
2007-08
影响因子：
8
通讯作者：
Hyekyoung Lee;Yong-Deok Kim;A. Cichocki;Seungjin Choi
中科院分区：
文献类型：
--
作者：
Hyekyoung Lee;Yong-Deok Kim;A. Cichocki;Seungjin Choi
在本文中，我们提出了一种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。这是我们之前使用非负矩阵分解 (NMF) 进行脑电图分类的工作的延伸。 BCI 竞赛中两个数据集的数值实验证实了该方法对于连续脑电图分类的有用行为。
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.