苏义博1,,,
马捃凯1,
丁威威1,
宋传承2
1.南京邮电大学电子与光学工程学院微电子学院 南京 210023
2.南京邮电大学贝尔英才学院 南京 210023
基金项目:国家自然科学基金(61977039)
详细信息
作者简介:黄丽亚：女，1972年生，教授，研究方向为物联网RFID技术、EDA技术以及通信网络的QoS性能研究
苏义博：男，1995年生，硕士生，研究方向为脑电信号分析及嵌入式系统应用
马捃凯：男，1996年生，硕士生，研究方向为脑电信号分析
丁威威：男，1996年生，硕士生，研究方向为经颅电刺激与人脑记忆力
通讯作者:苏义博 2524470353@qq.com
中图分类号:TN911.7; TP391.4计量
文章访问数:1426
HTML全文浏览量:519
PDF下载量:65
被引次数:0
出版历程
收稿日期:2019-11-04
修回日期:2020-03-04
网络出版日期:2020-03-20
刊出日期:2020-10-13
Research on Support Tensor Machine Based on Synchronous Brain Network for Emotion Classification
Liya HUANG1,Yibo SU1,,,
Junkai MA1,
Weiwei DING1,
Chuancheng SONG2
1. School of Electronic and Optical Engineering & Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2. Bell Honors Shool, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Funds:The National Natural Science Foundation of China (61977039)
摘要
摘要:一直以来，情绪是心理学、教育学、信息科学等多个学科的研究热点，脑电信号(EEG)因其客观、不易伪装的特点，在情绪识别领域受到广泛关注。由于人类情绪是大脑多个脑区相互作用产生的，该文提出一种基于同步性脑网络的支持张量机情绪分类算法(SBN-STM)，该算法采用相位锁定值(PLV)构建了同步性脑网络，分析多导联脑电信号之间的同步性和相关性，并生成2阶张量序列作为训练集，运用支持张量机(STM)模型实现正负情绪的二分类。该文基于DEAP脑电情绪数据库，详细分析了同步性脑网络张量序列的选取方法，最佳张量序列窗口的大小和位置，解决了传统情绪分类算法特征冗余的问题，提高了模型训练速度。仿真实验表明，基于支持张量机的同步性脑网络分类方法的情绪准确率优于支持向量机、C4.5决策树、人工神经网络、K近邻等以向量为特征的情绪分类模型。
关键词:情绪分类/
同步性脑网络/
支持张量机/
相位锁定值
Abstract:Emotion has always been a research hot spot in many disciplines such as psychology, education, and information science. Electro EncephaloGram(EEG) signal has received extensive attention in the field of emotion recognition because of its objective and not easy to disguise. Since human emotions are generated by the interaction of multiple brain regions in the brain, an algorithm of Support Tensor Machine based on Synchronous Brain Network (SBN-STM) for emotion classification is proposed. The algorithm uses Phase Locking Value (PLV) to construct a synchronous brain network, in order to analyze the synchronization and correlation between multi-channel EEG signals, and generate a second-order tensor sequence as a training set. The Support Tensor Machine (STM) model can distinguish a two-category of positive and negative emotions. Based on the DEAP EEG emotion database, this paper analyzes the selection method of synchronic brain network tensor sequence, the research on the size and position of the optimal tensor sequence window solves the problem of traditional emotion classification algorithm which always exists feature redundancy, and improves the model training speed. The results show that the accuracy of the emotional classification method based on SBN-STM is better than support vector machine, C4.5 decision tree, artificial neural network, and K-nearest neighbor which using vectors as input feature.
Key words:Emotion classification/
Synchronous Brain Network (SBN)/
Support Tensor Machine (STM)/
Phase Locking Value (PLV)
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