Construction of the EEG Emotion Judgment System Using Concept Base of EEG Features

机译：利用脑电特征概念库构建脑电情感判断系统

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

For a robot converse naturally with a human, it must be able to accurately gauge the emotional state of the person. Techniques for estimating emotions of a person from facial expressions, intonation and speech content have been proposed. This paper presents a technique for judging the emotion of a person using EEGs. The system of judging the emotion from EEGs is called EEG Emotion Judgment System, and constructed Concept Base for reducing noise in this paper. Accuracy of emotion judgment using EEG features of all subjects was 57.6% and using leave-one-out cross validation was 30.8%. Although performance accuracy remains low, continued development is required through further development of methods for both reducing different variety of noise mixed in with EEGs.

机译：对于与人自然对话的机器人，它必须能够准确地衡量人的情绪状态。已经提出了从面部表情，语调和语音内容估计人的情绪的技术。本文提出了一种使用脑电图判断人的情绪的技术。从脑电图判断情绪的系统称为脑电图情感判断系统，并建立了降低噪声的概念库。使用所有受试者的脑电图特征进行情感判断的准确度为57.6％，使用留一法交叉验证的准确度为30.8％。尽管性能精度仍然很低，但是需要通过进一步开发方法来减少与EEG混合在一起的各种噪声来继续开发。

著录项

来源
《International conference on artificial intelligence》|2015年|485-490|共6页
会议地点
作者
Mayo Morimoto; Misako Imono; Seiji Tsuchiya; Hirokazu Watabe;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类
关键词
EEG; judging emotion; concept base;

机译：脑电图;判断情感;概念基础;

相似文献

外文文献
中文文献
专利

1. Emotion Judgment System by EEG Based on Concept Base of EEG Features [J] . Mayo Morimoto, Misako Imono, Seiji Tsuchiya, 中兴通讯技术（英文版） . 2017,第0z2期

机译：基于脑电特征概念库的脑电情感判断系统
2. A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison [J] . Hamada Mohamed, Zaidan B. B., Zaidan A. A. Journal of medical systems . 2018,第9期

机译：基于情感分类的人EEG脑信号系统审查，特征提取，脑状况，群体比较
3. Learning CNN features from DE features for EEG-based emotion recognition [J] . Hwang Sunhee, Hong Kibeom, Son Guiyoung, Pattern Analysis and Applications . 2020,第3期

机译：从基于脑电图的情感识别学习CNN功能
4. Construction of the EEG Emotion Judgment System Using Concept Base of EEG Features [C] . Mayo Morimoto, Misako Imono, Seiji Tsuchiya, International conference on artificial intelligence . 2015

机译：使用EEG功能概念基础构建脑电图情感判断系统
5. THE DEVELOPMENT OF AN AUTOMATIC EEG ANALYSIS SYSTEM UTILIZING AN INTERACTIVE FEATURE EXTRACTION PROGRAM BASED ON MODIFIED HAAR COEFFICIENTS. [D] . BARNETT, WILLIAM JACKSON. 1972

机译：利用基于改进的Haar系数的交互式特征提取程序开发自动脑电分析系统。
6. Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms [O] . Kei Suzuki, Tipporn Laohakangvalvit, Ryota Matsubara, 2021

机译：使用特征提取和特征选择算法构建基于EEG / HRV索引的情绪估计模型
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

Construction of the EEG Emotion Judgment System Using Concept Base of EEG Features

摘要

著录项

相似文献

相关主题

期刊订阅