Classification of Happiness and Sadness Based on Portable EEG Devices
CSTR:
Author:
  • Article
  • | |
  • Metrics
  • |
  • Reference [25]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    There is an important application value for the research of vehicle active safety technology through the recognition of drivers’ emotional state. In this study, seventeen subjects’ frontal dual-channel EEG signals were collected by emotional video induction method, and EEG characteristics of different emotions were extracted. After dimensionality reduction, the data were classified by multiple classifiers. The results show that compared with single-core classifier and ensemble learning classifier, Gradient Boosting Decision Tree (GBDT) algorithm has the highest recognition accuracy of happiness and sadness. This study provides a new method for real-time monitoring and recognition of drivers’ emotional state, and provides a theoretical guarantee for improving driving safety.

    Reference
    [1] Megías A, Maldonado A, Cándido A, et al. Emotional modulation of urgent and evaluative behaviors in risky driving scenarios. Accident Analysis & Prevention, 2011, 43(3):813-817
    [2] NHTSA. Traffic safety facts 2007:A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system. Washington DC:National Highway Traffic Safety Administration, 2007.
    [3] Lin YP, Wang CH, Wu TL, et al. EEG-based emotion recognition in music listening:A comparison of schemes for multiclass support vector machine. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Taipei, China. 2009. 489-492.
    [4] Schaaff K. EEG-based emotion recognition. Diplomarbeit am institut fur algorithmen und kognitive systeme[Thesis]. Karlsruhe:Universitat Karlsruhe, 2008.
    [5] Li M, Lu BL. Emotion classification based on gamma-band EEG. Proceedings of 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis, MN, USA. 2009. 1323-1326.
    [6] Liu YS, Sourina O, Hafiyyandi MR. EEG-based emotion-adaptive advertising. Proceedings of 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. Geneva, Switzerland. 2013. 843-848.
    [7] Ishino K, Hagiwara M. A feeling estimation system using a simple electroencephalograph. Proceedings of 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-system Security and Assurance. Washington, DC, USA. 2003. 4204-4209.
    [8] Schaaff K, Schultz T. Towards an EEG-based emotion recognizer for humanoid robots. Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication. Toyama, Japan. 2009. 792-796.
    [9] 冯正直, 张大均. 中国版SCL-90的效度研究. 第三军医大学学报, 2001, 23(4):481-483.[doi:10.3321/j.issn:1000-5404.2001.04.038
    [10] 郑晓华, 李延知. 状态-特质焦虑问卷. 中国心理卫生杂志, 1997, 11(4):219-220.[doi:10.3321/j.issn:1000-6729.1997.04.009
    [11] 张明园. 精神科评定量表手册. 2版. 长沙:湖南科学技术出版社, 1998.
    [12] 徐鹏飞, 黄宇霞, 罗跃嘉. 中国情绪影像材料库的初步编制和评定. 中国心理卫生杂志, 2010, 24(7):551-554, 561.[doi:10.3969/j.issn.1000-6729.2010.07.017
    [13] Valenzi S, Islam T, Juric P, et al. Individual classification of emotions using EEG. Journal of Biomedical Science and Engineering, 2014, 7(8):604-620.[doi:10.4236/jbise.2014.78061
    [14] 王一牛, 周立明, 罗跃嘉. 汉语情感词系统的初步编制及评定. 中国心理卫生杂志, 2008, 22(8):608-612.[doi:10.3321/j.issn:1000-6729.2008.08.014
    [15] Schaefer A, Nils F, Sanchez X, et al. Assessing the effectiveness of a large database of emotion-eliciting films:A new tool for emotion researchers. Cognition & Emotion, 2010, 24(7):1153-1172
    [16] Gross JJ, Levenson RW. Emotion elicitation using films. Cognition and Emotion, 1995, 9(1):87-108.[doi:10.1080/02699939508408966
    [17] 林凤涛, 陈明奎. 二阶统计量的盲源分离研究. 噪声与振动控制, 2008, 28(2):7-9, 14.[doi:10.3969/j.issn.1006-1355.2008.02.003
    [18] Katz MJ. Fractals and the analysis of waveforms. Computers in Biology and Medicine, 1988, 18(3):145-156.[doi:10.1016/0010-4825(88)90041-8
    [19] Coan JA, Allen JJB. Frontal EEG asymmetry as a moderator and mediator of emotion. Biological Psychology, 2004, 67(1-2):7-50.[doi:10.1016/j.biopsycho.2004.03.002
    [20] 任亚莉. 基于脑电的脑-机接口系统. 中国组织工程研究与临床康复, 2011, 15(4):749-752
    [21] 刘爽, 仝晶晶, 杨佳佳, 等. 基于脑电同源样本捆绑法的情绪识别研究. 中国生物医学工程学报, 2016, 35(3):272-277.[doi:10.3969/j.issn.0258-8021.2016.03.003
    [22] 李昕, 蔡二娟, 田彦秀, 等. 一种改进脑电特征提取算法及其在情感识别中的应用. 生物医学工程学杂志, 2017, 34(4):510-517, 528
    [23] 钟铭恩, 吴平东, 彭军强, 等. 基于脑电信号的驾驶员情绪状态识别研究. 中国安全科学学报, 2011, 21(9):64-69.[doi:10.3969/j.issn.1003-3033.2011.09.011
    [24] Nie D, Wang XW, Shi LC, et al. EEG-based emotion recognition during watching movies. Proceedings of 2011 5th International IEEE/EMBS Conference on Neural Engineering. Cancun, Mexico. 2011. 667-670.
    [25] Zheng WL, Zhu JY, Peng Y, et al. EEG-based emotion classification using deep belief networks. Proceedings of 2014 IEEE International Conference on Multimedia and Expo. Chengdu, China. 2014. 1-6.
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

姚娟娟,路堃,许金秀.基于便携式脑电设备的快乐和悲伤情绪分类.计算机系统应用,2020,29(5):233-238

Copy
Share
Article Metrics
  • Abstract:2152
  • PDF: 2192
  • HTML: 2092
  • Cited by: 0
History
  • Received:September 04,2019
  • Revised:October 08,2019
  • Online: May 07,2020
  • Published: May 15,2020
Article QR Code
You are the first992235Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063