Social anxiety prediction based on ERP features: A deep learning approach.

Journal: Journal of affective disorders
PMID: 39236887

Abstract

BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological activities. However, existing methods mainly focus on extracting EEG features during resting states, with limited use of psychologically features like Event-Related Potential (ERP) in task-related states for anxiety detection in deep learning frameworks.

Authors

  • Xiaodong Tian
    School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Lingkai Zhu
    School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Mingxian Zhang
    School of Education and Psychology, University of Jinan, Jinan, China.
  • Songling Wang
    School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Yi Lu
    Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-Sen University, 26th Yuancun the Second Road, Guangzhou, 510655, Guangdong Province, China.
  • Xiaolei Xu
    Department of Biochemistry and Molecular Biology, Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN 55905, USA. xu.xiaolei@mayo.edu.
  • Weikuan Jia
    School of Information Science and Engineering, Shandong Normal University, Shandong, China.
  • Yuanjie Zheng
  • Sutao Song
    School of Education and Psychology, University of Jinan, Jinan, China. Electronic address: sep_songst@ujn.edu.cn.

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