Motor imagery EEG signals analysis based on Bayesian network with Gaussian distribution
Motor imagery EEG signals analysis based on Bayesian network with Gaussian distribution
复制标题
基于高斯分布贝叶斯网络的运动想象脑电信号分析
DOI：
10.1016/j.neucom.2015.05.133
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发表时间：
2016-05-05
期刊：
影响因子：
6
通讯作者：
Long, Jun
中科院分区：
文献类型：
--
作者：
He, Lianghua;Liu, Bin;Long, Jun
脑-机接口作为一种新型的脑-机通信通道，近年来受到了越来越多的关注。提出了一种基于贝叶斯网络的多运动想象任务分析方法。一方面，以通道的物理位置信息和运动想象类别信息均值为约束条件构建BN结构;另一方面，采用连续高斯分布模型对贝叶斯网络节点进行建模，而不是传统方法中的离散化变量，更能反映脑电信号的真实的特征。最后，利用网络结构和边缘推理得分构造SVM分类器。在BCI竞赛数据集BCI伊利亚和我们实验室收集的数据集上的实验结果表明，基于边缘选择的两种方法的平均准确率分别为93%和88%，与现有的方法相比有了较大的提高。(C)2015 Elsevier B. V.版权所有。
As a novel communication channel from brain to machine, the research of Brain-computer interfacing has attracted more and more attention recently. In this paper, a novel method based on Bayesian Network is proposed to analyze multi-motor imagery tasks. On the one hand, the information of channels physical positions and motor imagery class information mean value are adopted as constrains in BN structure construction. On the other hand, continuous Gaussian distribution model is used to model the Bayesian network nodes other than discretizing variable in traditional methods, which would reflect the real character of EEG signals. Finally, the network structure and edge inference score are used to construct SVM classifier. Experimental results on the BCI competition dataset BCI Ilia and our own lab collected dataset show that the average accuracy of the two experiments are 93% and 88% based on edge selection, which are better comparing to current methods. (C) 2015 Elsevier B.V. All rights reserved.