自动化学报2016，Vol.42Issue(8)：1215-1226,12.DOI:10.16383/j.aas.2016.c150461
分层向量自回归的多通道脑电信号的特征提取研究
Multi-channel EEG Feature Extraction Using Hierarchical Vector Autoregression
摘要
Abstract
Feature extraction and classification of electroencephalogram (EEG) signals is a core part of brain-computer interface (BCI). For multi-channel EEG signal and high dimension of feature vector of BCI system, a novel EEG signal recognition method called hierarchical vector autoregression (HVAR) is presented, which extracts EEG feature using regression coefficient of HVAR model and linear support vector machine (SVM). It overcomes the limitations of the autoregression (AR) model that can be used to extract the single channel EEG only, and effectively avoids the vector autoregression (VAR) model sharing a same delay for all channels. Our contribution is that regularization is added on the traditional VAR model and a reasonable hierarchical structure is adopted. It effectively compresses parameter space of VAR model. In this paper, HVAR model is used for EEG data classification for the first time. Experimental results show that the recognition accuracy of extracted feature of HVAR model using a 2 lag order multi-channel is higher than that of AR model of 6 lag order. So low-level HVAR model can describe the portrayed temporal relationship of EEG well. This shows HVAR may be a novel method to portray EEG signal, which has reference significance to other multi-channel time-series.关键词
脑机接口/脑电信号/分层向量自回归模型/特征提取/近邻梯度Key words
Brain-computer interface (BCI)/electroencephalogram (EEG)/hierarchical vector autoregression (HVAR)/feature extraction/proximal gradient method引用本文复制引用
王金甲,陈春..分层向量自回归的多通道脑电信号的特征提取研究[J].自动化学报,2016,42(8):1215-1226,12.基金项目
Manuscript received July 20,2015 （）
accepted February 18,2016国家自然科学基金(61473339),中国博士后科学基金(2014M561202),河北省博士后专项基金(B2014010005),首批“河北省青年拔尖人才”项目([2013]17)资助 （61473339）
