Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data.

Journal: Journal of affective disorders
Published Date: May 15, 2019

Abstract

OBJECTIVE: Major depression disorder (MDD) is one of the most prevalent mental disorders worldwide. Diagnosing depression in the early stage is crucial to treatment process. However, due to depression's comorbid nature and the subjectivity in diagnosis, an early diagnosis could be challenging. Recently, machine learning approaches have been used to process Electroencephalography (EEG) and neuroimaging data to facilitate the diagnosis. In the present study, we used a multimodal machine learning approach involving EEG, eye tracking and galvanic skin response data as input to classify depression patients and healthy controls.

Authors

  • Xinfang Ding
    Department of Medical Psychology, School of Medical Humanities, Capital Medical University, Beijing, China.
  • Xinxin Yue
    Peking University Sixth Hospital, Beijing, China.
  • Rui Zheng
    HKUST-DT System and Media Laboratory, Hong Kong University of Science and Technology, HongKong.
  • Cheng Bi
    Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China; Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China.
  • Dai Li
    Adai Technology (Beijing) Ltd., Co, Beijing, China.
  • Guizhong Yao
    Peking University Sixth Hospital, Beijing, China. Electronic address: yaoguizhongpku@163.com.

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