您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（工学版）》

山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (4): 35-39.doi: 10.6040/j.issn.1672-3961.0.2019.679

• • 上一篇    

基于Bi-LSTM的脑电情绪识别

刘帅1,2,王磊1,2*,丁旭涛1,2   

  1. 1. 河北工业大学省部共建电工装备可靠性与智能化国家重点实验室, 天津 300130;2. 河北工业大学河北省电磁场与电器可靠性重点实验室, 天津 300130
  • 发布日期:2020-08-13
  • 作者简介:刘帅(1993— ),男,山东济南人,硕士研究生,主要研究方向为深度学习,脑认知与神经工程. E-mail:847075008@qq.com. *通信作者简介:王磊(1978— ),男,天津人,副教授,博士,主要研究方向为生物电信号分析,脑机接口. E-mail:murhythm@qq.com
  • 基金资助:
    国家自然科学基金资助项目(31300818);河北省高等学校科学技术研究项目(QN2016097)

Emotional EEG recognition based on Bi-LSTM

LIU Shuai1,2, WANG Lei1,2*, DING Xutao1,2   

  1. 1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China;
    2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China
  • Published:2020-08-13

PDF (PC)

647

摘要： 为解决脑电(electroencephalogram, EEG)情绪识别这一项具有挑战性的任务,提出一种基于双向长短时记忆网络(bidirectional long short-term memory, Bi-LSTM)的脑电情绪分类模型并探索大脑情绪机制,唤醒度准确率最高为76.78%,效价度准确率最高为77.28%,与其他模型比较,Bi-LSTM模型在脑电情绪识别上有出色的表现。通过Bi-LSTM模型对比不同频段、脑区和特征疏密度的准确率来探索大脑情绪机制,表明大脑中情绪相关性最高的频段、脑区和特征疏密度分别为α和β、顶叶区与额叶区、50和15。

关键词: 脑电情绪识别, 深度学习, 情绪, 脑电, 双向长短时记忆网络

Abstract: To solved a challenging task of emotional electroencephalogram(EEG)recognition, this study proposed a bidirectional long short-term memory(Bi-LSTM)EEG classification model and explored the emotional mechanism of the brain, with the highest arousal accuracy of 76.78% and the highest valence accuracy of 77.28%. The Bi-LSTM model, compared with other models, had excellent performances in the recognition of emotional EEG. The Bi-LSTM model was used to explore the brain emotion mechanism by comparing the accuracy of different frequency bands, brain regions and feature density, and the results showed that the frequency bands, brain regions and feature density with the highest emotional correlation in the brain were respectively the α and β regions, Parietal Lobe and Frontal Lobe, 50 and 15.

Key words: EEG emotional recognition, deep learning, emotion, EEG, Bi-LSTM

中图分类号: 

  • TP183
[1] 乔建中. 情绪研究: 理论与方法[M]. 南京:南京师范大学出版社, 2003:1-10.
[2] YANG X, LIU J, CHEN Z, et al. Semi-supervised learning of dialogue acts using sentence similarity based on word embeddings[C] // International Conference on Audio. Shanghai, China:IEEE, 2015:882-886.
[3] ADEYANJU I A, OMIDIORA E O, OYEDOKUN O F. Performance evaluation of different support vector machine kernels for face emotion recognition[C] //SAI Intelligent Systems Conference 2015. London, UK:IEEE, 2015:804-806.
[4] 聂聃, 段若男. 基于脑电的情绪识别研究综述[J]. 中国生物医学工程学报, 2012, 31(4):595-606. NIE Dan, DUAN Ruonan. A survey on EEG based emotion recognition[J].Chinese Journal of Biomedical Engineering, 2012, 31(4):595-606.
[5] QIAO R, QING C, ZHANG T, et al. A novel deep-learning based framework for multi-subject emotion recognition[C] //International Conference on Information. Dalian, China:IEEE, 2017:181-185.
[6] 阚威, 李云. 基于LSTM的脑电情绪识别模型[J]. 南京大学学报(自然科学), 2019, 55(1):116-122. KAN Wei, LI Yun. Emotion recognition from EEG signals by using LSTM recurrent neural networks[J]. Journal of Nanjing University(Natural Sciences), 2019, 55(1):116-122.
[7] SONG T, ZHENG W, SONG P, et al. EEG emotion recognition using dynamical graph convolutional neural networks[J]. IEEE Transactions on Affective Computing, 2018(99):1-10.
[8] LI X, SONG D, ZHANG P, et al. Emotion recognition from multi-channel EEG data through convolutional recurrent neural network[C] //2016 IEEE International Conference on Bioinformatics and Biomedicine(BIBM). Shenzhen, China:IEEE, 2016:352-359.
[9] (¨overO)ZAL YILDIRIM. A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification[J]. Computers in Biology & Medicine, 2018, 96(1):189-202.
[10] ADAM K, SMAGULOVA K, JAMES A P. Memristive LSTM network hardware architecture for time-series predictive modeling problem[C] //2018 IEEE Asia Pacific Conference on Circuits and Systems(APCCAS). Chengdu, China:IEEE, 2018:459-462.
[11] KOELSTRA S, MUHL C, SOLEYMANI M, et al. DEAP: a database for emotion analysis using physiological signals[J]. IEEE Transactions on Affective Computing, 2012, 3(1):18-31.
[12] MURRAY I R, ARNOTT J L. Toward the simulation of emotion in synthetic speech: a review of the literature on human vocal emotion[J]. The Journal of the Acoustical Society of America, 1993, 93(2):1097-1108.
[13] KONG W, DONG Z Y, JIA Y, et al. Short-Term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2018(10):841-851.
[14] HOW D N T, SAHARI K S M, HU Y, et al. Multiple sequence behavior recognition on humanoid robot using long short-term memory(LSTM)[C] //IEEE International Symposium on Robotics & Manufacturing Automation. Kuala Lumpur, Malaysia: IEEE, 2014: 109-114.
[15] XIAO R, CUI X, ZHOU P, et al. LSTM based on the classification of emotion about user evaluation on shopping site[C] //2016 International Conference on Identification, Information and Knowledge in the Internet of Things(IIKI). Beijing, China:IEEE, 2016:52-53.
[16] DAIMI S N, SAHA G. Classification of emotions induced by music videos and correlation with participants, rating[J]. Expert Systems with Applications, 2014, 41(13): 6057-6065.
[17] MERT A, AKAN A. Emotion recognition from EEG signals by using multivariate empirical mode decomposition[J]. Pattern Analysis and Applications, 2016, 21(1):81-89.
[18] GUPTA R, LAGHARI K U R, FALK T H. Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization[J]. Neurocomputing, 2016, 174(2):875-884.
[19] ATKINSON J, CAMPOS D. Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers[J]. Expert Systems with Applications, 2016, 47(1):35-41.
[1] 蔡国永, 贺歆灏, 储阳阳. 基于空间注意力和卷积神经网络的视觉情感分析[J]. 山东大学学报 (工学版), 2020, 50(4): 8-13.
[2] 李春阳,李楠,冯涛,王朱贺,马靖凯. 基于深度学习的洗衣机异常音检测[J]. 山东大学学报 (工学版), 2020, 50(2): 108-117.
[3] 陈德蕾,王成,陈建伟,吴以茵. 基于门控循环单元与主动学习的协同过滤推荐算法[J]. 山东大学学报 (工学版), 2020, 50(1): 21-27,48.
[4] 张继,金翠,王洪元,陈首兵. 基于奇异值分解行人对齐网络的行人重识别[J]. 山东大学学报 (工学版), 2019, 49(5): 91-97.
[5] 万鹏. 基于F-PointNet的3D点云数据目标检测[J]. 山东大学学报 (工学版), 2019, 49(5): 98-104.
[6] 梁志祥,刘晓明,牟颖,刘玉田. 基于深度学习的新能源爬坡事件预测方法[J]. 山东大学学报 (工学版), 2019, 49(5): 24-28.
[7] 刘玉田, 孙润稼, 王洪涛, 顾雪平. 人工智能在电力系统恢复中的应用综述[J]. 山东大学学报 (工学版), 2019, 49(5): 1-8.
[8] 李力钊,蔡国永,潘角. 基于C-GRU的微博谣言事件检测方法[J]. 山东大学学报 (工学版), 2019, 49(2): 102-106, 115.
[9] 张成彬,赵慧,曹宗钰. 基于深度学习的车身网络KWP2000协议漏洞挖掘[J]. 山东大学学报 (工学版), 2019, 49(2): 17-22.
[10] 侯霄雄,许新征,朱炯,郭燕燕. 基于AlexNet和集成分类器的乳腺癌计算机辅助诊断方法[J]. 山东大学学报 (工学版), 2019, 49(2): 74-79.
[11] 谢志峰,吴佳萍,马利庄. 基于卷积神经网络的中文财经新闻分类方法[J]. 山东大学学报(工学版), 2018, 48(3): 34-39.
[12] 唐乐爽,田国会,黄彬. 一种基于DSmT推理的物品融合识别算法[J]. 山东大学学报(工学版), 2018, 48(1): 50-56.
[13] 周福娜,高育林,王佳瑜,文成林. 基于深度学习的缓变故障早期诊断及寿命预测[J]. 山东大学学报(工学版), 2017, 47(5): 30-37.
[14] 李庆宾,王晓东. 分数阶情绪模型的终端滑模控制混沌同步[J]. 山东大学学报(工学版), 2017, 47(3): 84-88.
[15] 郝崇清,王志宏. 基于复杂网络的癫痫脑电分类与分析[J]. 山东大学学报(工学版), 2017, 47(3): 8-15.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(工学版)》编辑部　
地址: 济南市山大南路27号（250100）　 电话: 0531-88366735　 E-mail: xbgxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发