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作者:

Kuai, Hongzhi (Kuai, Hongzhi.) | Yang, Yang (Yang, Yang.) | Chen, Jianhui (Chen, Jianhui.) | Zhang, Xiaofei (Zhang, Xiaofei.) | Yan, Jianzhuo (Yan, Jianzhuo.) | Zhong, Ning (Zhong, Ning.)

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CPCI-S EI

摘要:

Emotion processing, playing an important role in our social interactions, is a sub-topic of social cognition. Significant differences in emotion perception and processing have been demonstrated between schizophrenia and normal people. Therefore, it is a very effective strategy to use the emotional stimulation as the core means to explore the difference between patients and normal people, and then to develop the discriminative model for patients with schizophrenia. In this paper, emotional images were used to stimulate the two groups (schizophrenia group and control group), and the electrophysiological signals during the experiment were recorded. In the feature extraction phase, the time-domain dynamics and the asymmetry of the hemisphere were considered at different stimulation stages. Finally, five effective machine learning methods were used to distinguish between schizophrenia and healthy controls under positive and negative emotional stimuli, respectively. The experimental results show that the two groups of event-related electrophysiological signals obtained by negative stimulation can be better distinguished than those obtained by positive stimulation. And, this phenomenon is more pronounced in the time window of first second after the stimulus appears. Meanwhile, the highest average F-score with 10-fold crossvalidation strategy can reach 0.994 by combining both support vector machine classifier and grid search methods.

关键词:

Schizophrenia Emotion Prediction Machine learning Electroencephalography

作者机构:

  • [ 1 ] [Kuai, Hongzhi]Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gumma 3710816, Japan
  • [ 2 ] [Zhong, Ning]Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gumma 3710816, Japan
  • [ 3 ] [Kuai, Hongzhi]Beijing Univ Technol, Int WIC Inst, Beijing, Peoples R China
  • [ 4 ] [Yang, Yang]Beijing Univ Technol, Int WIC Inst, Beijing, Peoples R China
  • [ 5 ] [Chen, Jianhui]Beijing Univ Technol, Int WIC Inst, Beijing, Peoples R China
  • [ 6 ] [Zhang, Xiaofei]Beijing Univ Technol, Int WIC Inst, Beijing, Peoples R China
  • [ 7 ] [Zhong, Ning]Beijing Univ Technol, Int WIC Inst, Beijing, Peoples R China
  • [ 8 ] [Chen, Jianhui]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 9 ] [Zhang, Xiaofei]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 10 ] [Yan, Jianzhuo]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 11 ] [Zhong, Ning]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 12 ] [Yang, Yang]Beijing Forestry Univ, Dept Psychol, Beijing, Peoples R China
  • [ 13 ] [Kuai, Hongzhi]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
  • [ 14 ] [Yang, Yang]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
  • [ 15 ] [Chen, Jianhui]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
  • [ 16 ] [Zhong, Ning]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China

通讯作者信息:

  • 钟宁

    [Zhong, Ning]Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gumma 3710816, Japan;;[Zhong, Ning]Beijing Univ Technol, Int WIC Inst, Beijing, Peoples R China;;[Zhong, Ning]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China;;[Zhong, Ning]Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China

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来源 :

BRAIN INFORMATICS

ISSN: 0302-9743

年份: 2019

卷: 11976

页码: 169-178

语种: 英文

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