Emotion Recognition Based on EEG Signals Using LIBSVM as the Classifier

  • Tian ChenEmail author
  • Sihang Ju
  • Fuji Ren
  • Mingyan Fan
  • Xin An
Conference paper
Part of the Studies in Distributed Intelligence book series (SDI)


In order to improve the emotion recognition rate, this paper proposes an electroencephalograph (EEG) emotion recognition model using a library for support vector machine (LIBSVM) as the classifier. In this paper, we collected EEG signals from 10 volunteers along with the participants’ self-assessment of their affective state after each stimulus, in terms of Valence and arousal. After these signals are filtered, we calculate the features of Lempel-Ziv complexity, wavelet detail coefficient, and the co-integration relationship degree first. At the same time, EMD is carried out to calculate the average approximate entropy of the first four Intrinsic Mode Functions (IMFs). At last, all the features extracted will input into the LIBSVM for training and testing, and complete emotion recognition. In this paper, two classifications are carried out on the two dimensions of Valence and Arousal, respectively. The experimental results show that the average emotional recognition rate is 83.64% and 75.11%, respectively, which proves that the proposed scheme has a certain feasibility.


Text Mining Entity Linking Topic Model Latent Dirichlet Allocation Semantic 



This work supported by The Key Program of the National Natural Science Foundation of China (Grant No. 61432004); The National Natural Science Foundation of China (Grant No. 61474035, 61502140); The fund of Affective Computing and Advanced Intelligent Machine Anhui Province Key Laboratory (Grant No. ACAIM180101); NSFC-Shenzhen Joint Foundation (Key Project) (Grant No. U1613217).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tian Chen
    • 1
    • 2
    Email author
  • Sihang Ju
    • 1
    • 2
  • Fuji Ren
    • 1
    • 2
    • 3
  • Mingyan Fan
    • 1
    • 2
  • Xin An
    • 1
    • 2
  1. 1.School of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina
  2. 2.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent MachineHefei University of TechnologyHefeiChina
  3. 3.Faculty of EngineeringThe University of TokushimaTokushimaJapan

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