Resting State EEG Based Depression Recognition Research Using Deep Learning Method

  • Wandeng Mao
  • Jing Zhu
  • Xiaowei LiEmail author
  • Xin Zhang
  • Shuting Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)


Deep learning has obtained state-of-the-art performance in many fields with its powerful ability of representation learning. However, unlike other data, EEG signals have temporal, spatial and frequency characteristics. For the EEG based depression detection, how to preserve these features when EEG signals are fed into neural networks and select appropriate network structure to extract the corresponding inherent patterns is a problem that needs to be solved. Inspired by the application of deep learning in image processing, we used the distance-based projection method and the non-distance projection method to construct EEG signals as inputs of neural networks. Four different networks were used to extract inherent structure from constructed data. As a result, CNN outperformed other networks, with the highest classification accuracy of 77.20% using the non-distance projection method and 76.14% using the distance-based projection method. The results demonstrate that application of deep learning methods in the research of depression is feasible.


Depression detection EEG Deep learning 



This work was supported by the National Basic Research Program of China (973 Program) [No. 2014CB744600]; the National Natural Science Foundation of China [Nos. 61632014, 61210010]; the International Cooperation Project of Ministry of Science and Technology [No. 2013DFA11140]; and the Program of Beijing Municipal Science & Technology Commission [No. Z171100000117005].


  1. 1.
    Debener, S., Beauducel, A., Nessler, D., Brocke, B., Heilemann, H., Kayser, J.: Is resting anterior EEG alpha asymmetry a trait marker for depression? Neuropsychobiology 41, 31–37 (2000)CrossRefGoogle Scholar
  2. 2.
    Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649. IEEE (2012)Google Scholar
  3. 3.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436 (2015)CrossRefGoogle Scholar
  4. 4.
    Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38, 5391–5420 (2017)CrossRefGoogle Scholar
  5. 5.
    Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448 (2015)
  6. 6.
    Carabez, E., Sugi, M., Nambu, I., Wada, Y.: Convolutional neural networks with 3D input for P300 identification in auditory brain-computer interfaces. Comput. Intell. Neurosci. 2017, 9 (2017)CrossRefGoogle Scholar
  7. 7.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)CrossRefGoogle Scholar
  8. 8.
    Cai, H., Sha, X., Han, X., Wei, S., Hu, B.: Pervasive EEG diagnosis of depression using deep belief network with three-electrodes EEG collector. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1239–1246. IEEE (2016)Google Scholar
  9. 9.
    Kroenke, K., Spitzer, R.L.: The PHQ-9: a new depression diagnostic and severity measure. Psychiatr. Ann. 32, 509–515 (2002)CrossRefGoogle Scholar
  10. 10.
    Hu, B., et al.: EEG-based cognitive interfaces for ubiquitous applications: Developments and challenges. IEEE Intell. Syst. 26, 46–53 (2011)CrossRefGoogle Scholar
  11. 11.
    Omel’chenko, V., Zaika, V.: Changes in the EEG-rhythms in endogenous depressive disorders and the effect of pharmacotherapy. Hum. Physiol. 28, 275–281 (2002)CrossRefGoogle Scholar
  12. 12.
    Snyder, J.P.: Map Projections–A Working Manual. US Government Printing Office, Washington (1987). Scholar
  13. 13.
    Alfeld, P.: A trivariate clough—tocher scheme for tetrahedral data. Comput. Aided Geom. Des. 1, 169–181 (1984)CrossRefGoogle Scholar
  14. 14.
    Luu, P., Ferree, T.: Determination of the HydroCel Geodesic Sensor Nets’ Average Electrode Positions and Their 10–10 International Equivalents. Inc, Technical Note (2005)Google Scholar
  15. 15.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
  16. 16.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRefGoogle Scholar
  17. 17.
    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  18. 18.
    Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wandeng Mao
    • 1
  • Jing Zhu
    • 1
  • Xiaowei Li
    • 1
    Email author
  • Xin Zhang
    • 1
  • Shuting Sun
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina

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