EEG feature selection method based on decision tree
EEG feature selection method based on decision tree
复制标题
基于决策树的脑电特征选择方法
DOI：
10.3233/bme-151397
复制
发表时间：
2015-01-01
影响因子：
1
通讯作者：
Miao, Jun
中科院分区：
文献类型：
--
作者：
Duan, Lijuan;Ge, Hui;Miao, Jun
本文旨在解决脑机接口（BCI）中的自动特征选择问题。为了自动化特征选择过程，我们提出了一种基于决策树（DT）的新型脑电图特征选择方法。在脑电图（EEG）信号处理过程中，采用基于主成分分析（PCA）的特征提取方法，通过搜索特征空间并自动选择最优特征来进行基于决策树的选择过程。考虑到脑电信号是一系列非线性信号，选择了一种广义线性分类器，即支持向量机（SVM）。为了测试所提方法的有效性，我们将基于决策树的脑电特征选择方法应用于BCI竞赛II数据集Ia，实验取得了令人鼓舞的结果。
This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.