计算机工程与应用2019，Vol.55Issue(7)：132-137,144,7.DOI:10.3778/j.issn.1002-8331.1809-0239
核张量子空间分解EEG特征提取方法研究
Kernel Tensor Subspace Decomposition-Based EEG Feature Extraction Method
摘要
Abstract
Aiming at the hypothesis of strict linear model between source signals and recorded EEG signals in the Common Spatial Patterns(CSP), an EEG feature extraction method based on Kernel Tensor Subspace Decomposition(KTSD)is proposed, which can give full play to the advantage of tensors in multidimensional and simultaneous processing. Firstly, the tensor of EEG data is generated, and the tensor decomposition problem is solved by using the least squares problem with quadratic equality constraints, subsequently the tensor is extended to the subspace to reduce the computational pressure. Finally, it is extended to the kernel space to enhance the discrimination ability by projecting data onto high-dimensional feature space. BCI competition III-3a data set is used in the experiment. The experimental results show that KTSD method can extract the corresponding features from EEG data of various motion imagery tasks, and obtain better classification results and operational efficiency.关键词
EEG数据/核张量/子空间/核空间Key words
EEG date/kernel tensor/subspace/kernel space分类
信息技术与安全科学引用本文复制引用
高煜妤,王柏娜..核张量子空间分解EEG特征提取方法研究[J].计算机工程与应用,2019,55(7):132-137,144,7.基金项目
重庆市科委项目(No.cstc2017jcyjAX0135). （No.cstc2017jcyjAX0135）
