Channel optimization and nonlinear feature extraction for Electroencephalogram signals classification
Channel optimization and nonlinear feature extraction for Electroencephalogram signals classification
复制标题
脑电信号分类的通道优化和非线性特征提取
DOI：
10.1016/j.compeleceng.2015.03.015
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
P. K. Kankar
中科院分区：
文献类型：
--
作者：
R. Upadhyay;A. Manglick;D. K. Reddy;P. K. Padhy;P. K. Kankar
在目前的工作中，提出了一种方法，用于自动警觉水平检测人脑的非线性特征的脑电信号（EEG）。警戒水平检测方法包括脑电通道选择、特征提取和分类三个步骤。从64个通道获得的EEG信号被细分为四个频率子带，即α、β、δ和θ。通道选择标准Maximum Energy to Shannon Entropyratios应用于每个频带以选择适当的EEG通道。从所选通道获得的EEG信号被进一步划分为频率子带，即α、β和α-β带。计算三个非线性特征，如Higuchi分形维数，Petrosian分形维数和去趋势波动分析，分别为每个频率子带准备三个特征向量。三种机器学习技术用于警戒水平检测，如支持向量机，最小二乘支持向量机和人工神经网络。
In present work, a methodology for automatic vigilance level detection of human brain using nonlinear features of Electroencephalogram (EEG) signals is presented. Vigilance level detection methodology consists of three steps, EEG channels selection, feature extraction and classification. EEG signals obtained from 64 channels are sub-divided into four frequency sub-bands i.e. alpha, beta, delta and theta. Channel selection criteriaMaximum Energy toShannon Entropyratiois applied on each frequency band to select appropriate EEG channels. EEG signals obtained from selected channels are further divided into frequency sub-bands i.e. alpha, beta and alpha–beta bands. Three nonlinear features such as Higuchi fractal dimension, Petrosian fractal dimension and Detrended Fluctuation Analysis are calculated to prepare three feature vectors respective to each frequency sub-bands. Three machine learning techniques are used for vigilance level detection such as Support Vector Machine, Least Square-Support Vector Machine and Artificial Neural Network.