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International Conference on Brain Informatics

BI 2017: Brain Informatics pp 244–253Cite as

Study on Depression Classification Based on Electroencephalography Data Collected by Wearable Devices

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10654))

Abstract

Depression has become a disease, which may threaten millions of families’ well-being. The current method of screening depression is subjective, labor-consuming and costly. Study on Electroencephalogram (EEG) has become a new direction to explore an objective, low-cost and accurate method to detect depression. In this paper, three-electrode EEG data of 158 subjects (90 depressed and 68 normal control) in resting state, and under audio stimulation (positive and negative) were collected and processed. After feature selection using Sequential Floating Forward Selection (SFFS), four popular classification methods were applied and classification accuracies were verified using 10-fold cross validation. Results have shown the accuracy of classification will be improved when male and female are classified separately. The highest accuracy of male and female classification are 91.98%, 79.76%, respectively, compare to 77.43% when the classification is processed as gender-free. The effective depressive features of male and female are also different, which may be caused by the differences of brain structure. This research suggests a possible pervasive method of depression classification for future clinical application.

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Acknowledgment

This work was supported by the National Basic Research Program of China (973 Program) (No. 2014CB744600), the National Natural Science Foundation of China (Grant No. 61632014, No. 61210010), Program of Beijing Municipal Science & Technology Commission (No. Z171100000117005), the Program of International S&T Cooperation of MOST (No. 2013DFA11140).

Author information

Authors and Affiliations

  1. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Hanshu Cai, Yanhao Zhang, Xiaocong Sha & Bin Hu

  2. Center of Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences (CEBSIT), Shanghai, 200031, China

    Bin Hu

  3. Beijing Institute for Brain Disorders, Beijing, 100069, China

    Bin Hu

  4. Army Staff Department, Western Theater Command, PLA, Lanzhou, 730000, China

    Yanhao Zhang

Authors
  1. Hanshu Cai
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  2. Yanhao Zhang
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  3. Xiaocong Sha
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  4. Bin Hu
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Corresponding author

Correspondence to Bin Hu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  2. Beijing Normal University, Beijing, China

    Yong He

  3. KTH Royal Institute of Technology and Karolinska Institute, Stockholm, Sweden

    Jeanette Hellgren Kotaleski

  4. University of California, San Diego, San Diego, California, USA

    Maryann Martone

  5. Chinese Academy of Sciences, Beijing, China

    Bo Xu

  6. Allen Institute for Brain Science, Seattle, Washington, USA

    Hanchuan Peng

  7. Wuhan National Lab Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qingming Luo

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Cai, H., Zhang, Y., Sha, X., Hu, B. (2017). Study on Depression Classification Based on Electroencephalography Data Collected by Wearable Devices. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-70772-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70771-6

  • Online ISBN: 978-3-319-70772-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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