EEG-Based Emotion Classification with Wavelet Entropy Feature

被引：3
|
作者：
Song, Xiaolin [1 ]
Kang, Qiaoju [2 ]
Tian, Zekun [2 ]
Yang, Yi [2 ]
Yang, Sihao [3 ]
Gao, Qiang [2 ]
Song, Yu [2 ]
机构：
[1] Tianjin Univ Technol, Engn Training Ctr, Tianjin, Peoples R China
[2] Tianjin Univ Technol, Tianjin Key Lab Control Theory & Applicat Complic, Tianjin, Peoples R China
[3] Tianjin Univ Technol, Natl Demonstrat Ctr Expt Mech Elect Engn Educ, Tianjin, Peoples R China
来源：
2020 CHINESE AUTOMATION CONGRESS (CAC 2020) | 2020年
关键词：
emotion recognition; wavelet entropy; classification accuracies;
D O I：
10.1109/CAC51589.2020.9326323
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Traditional emotion recognition methods are mainly based on voice, expression and body movement. These physiological signals or facial expressions may hardly reveal inner emotions. In this paper, the wavelet entropy (WE) was utilized to represent the characteristics associated with emotional states. The average classification accuracies of positive, neutral and negative emotions are 70.65%, 70.53% and 70.28%, respectively. In order to demonstrate the effectiveness of the proposed method, the comparison experiments were carried out by the power spectrum density (PSD) feature and approximate entropy (ApEn) feature. The average classification accuracies are 70.49% (WE), 66.93% (PSD) and 64.44% (%pEn), respectively. The results indicate that the wavelet entropy feature performs better than the other two features for Electroencephalogram (EEG) based emotion recognition.
引用
收藏
页码：5685 / 5689
页数：5
相关论文
共 50 条
  • [1] Differential Entropy Feature for EEG-Based Emotion Classification
    Duan, Ruo-Nan
    Zhu, Jia-Yi
    Lu, Bao-Liang
    2013 6TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2013, : 81 - 84
  • [2] EEG-based human emotion recognition using entropy as a feature extraction measure
    Patel P.
    Raghunandan R.
    Annavarapu R.N.
    Brain Informatics, 2021, 8 (01)
  • [3] EEG-based Automatic Emotion Recognition: Feature Extraction, Selection and Classification Methods
    Ackermann, Pascal
    Kohlschein, Christian
    Bitsch, Jo Agila
    Wehrle, Klaus
    Jeschke, Sabina
    2016 IEEE 18TH INTERNATIONAL CONFERENCE ON E-HEALTH NETWORKING, APPLICATIONS AND SERVICES (HEALTHCOM), 2016, : 159 - 164
  • [4] Attention with kernels for EEG-based emotion classification
    Kuang, Dongyang
    Michoski, Craig
    NEURAL COMPUTING & APPLICATIONS, 2023, 36 (10): : 5251 - 5266
  • [5] EEG-Based Familiar and Unfamiliar Face Classification Using Differential Entropy Feature
    Liu, Guoyang
    Di Zhang
    Tian, Lan
    Zhou, Weidong
    PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON HUMAN-MACHINE SYSTEMS (ICHMS), 2021, : 190 - 192
  • [6] EEG-based emotion recognition utilizing wavelet coefficients
    Momennezhad, Ali
    MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 77 (20) : 27089 - 27106
  • [7] Classification of EEG-based Emotion for BCI Applications
    Mohammadpour, Mostafa
    Hashemi, Seyyed Mohammad Reza
    Houshmand, Negin
    2017 ARTIFICIAL INTELLIGENCE AND ROBOTICS (IRANOPEN), 2017, : 127 - 131
  • [8] EEG-based emotion recognition utilizing wavelet coefficients
    Ali Momennezhad
    Multimedia Tools and Applications, 2018, 77 : 27089 - 27106
  • [9] Attention with kernels for EEG-based emotion classification
    Dongyang Kuang
    Craig Michoski
    Neural Computing and Applications, 2024, 36 : 5251 - 5266
  • [10] EEG-Based Emotion Recognition Using Wavelet Features
    Zhou, Zhengjie
    Jiang, Huiping
    Song, Xiaoyuan
    2014 5TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS), 2014, : 585 - 588
12345