Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 1
57-60,74

Fast sample entropy electroencephalogram emotion analysis of double sliding coarse granulation

Zhu Yongsheng
Zhong Qinghua
Cai Dongli
Liao Jinxiang
School of Physics & Telecommunication Engineering, South China Normal University, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2019.10.0593

Abstract

Aiming at the problems of traditional single-scale sample entropy that couldn't be obvious to extract electroencephalogram(EEG) sequence features, multi-scale entropy would miss important information in the coarse granulation process that decreased the performance of emotion classification, and the efficiency of sample entropy algorithm was not high, this paper proposed a multi-scale fast sample entropy EEG feature extraction method based on double sliding mean coarse granulation. Firstly, it processed the double sliding mean coarse-grained EEG signals in multiple scales because of the difference of different emotional EEG signals. Secondly, it used the fast sample entropy algorithm to extract sample entropy values of different time scales as eigenvectors. Lastly, it used the random forest(RF) classification model to identify different emotional states. This paper studied the proposed method in DEAP, a multi-mode standard emotion database, and it was found that the frontal area of the brain and the right brain were relatively sensitive to emotions, and the positive, neutral and negative emotions achieved an average classification accuracy of 88.75% in the lateral frontal area of the brain. Experimental results show that the proposed method can effectively extract EEG features and ensure the efficiency of the algorithm.

Foundation Support

国家自然科学基金资助项目(61871433)
广东省优秀青年教师培养计划资助项目(YQ2015046)
广州市珠江科技新星资助项目(201610010199)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.10.0593
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 1
Section: Algorithm Research & Explore
Pages: 57-60,74
Serial Number: 1001-3695(2021)01-011-0057-04

Publish History

[2021-01-05] Printed Article

Cite This Article

朱永升, 钟清华, 蔡冬丽, 等. 二次滑动粗粒化的快速样本熵脑电情感分析 [J]. 计算机应用研究, 2021, 38 (1): 57-60,74. (Zhu Yongsheng, Zhong Qinghua, Cai Dongli, et al. Fast sample entropy electroencephalogram emotion analysis of double sliding coarse granulation [J]. Application Research of Computers, 2021, 38 (1): 57-60,74. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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