Medical & Biological Engineering & Computing

, Volume 57, Issue 6, pp 1341–1352 | Cite as

EEG-based mild depression recognition using convolutional neural network

  • Xiaowei Li
  • Rong LaEmail author
  • Ying Wang
  • Junhong Niu
  • Shuai Zeng
  • Shuting Sun
  • Jing Zhu
Original Article


Electroencephalography (EEG)–based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infancy, especially in effective monitoring and quantitative measure aspects. Aiming at mild depression recognition, this study proposed a computer-aided detection (CAD) system using convolutional neural network (ConvNet). However, the architecture of ConvNet derived by trial and error and the CAD system used in clinical practice should be built on the basis of the local database; we therefore applied transfer learning when constructing ConvNet architecture. We also focused on the role of different aspects of EEG, i.e., spectral, spatial, and temporal information, in the recognition of mild depression and found that the spectral information of EEG played a major role and the temporal information of EEG provided a statistically significant improvement to accuracy. The proposed system provided the accuracy of 85.62% for recognition of mild depression and normal controls with 24-fold cross-validation (the training and test sets are divided based on the subjects). Thus, the system can be clinically used for the objective, accurate, and rapid diagnosis of mild depression.

Graphical abstract

The EEG power of theta, alpha, and beta bands is calculated separately under trial-wise and frame-wise strategies and is organized into three input forms of deep neural networks: feature vector, images without electrode location (spatial information), and images with electrode location. The role of EEG’s spectral and spatial information in mild depression recognition is investigated through ConvNet, and the role of EEG’s temporal information is investigated using different architectures to aggregate temporal features from multiple frames. The ConvNet and models for aggregating temporal features are transferred from the state-of-the-art model in mental load classification.


EEG Mild depression Convolutional neural network Transfer learning Classification 


Funding information

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, and No. 61402211], the Fundamental Research Funds for the Central Universities [No. lzujbky-2017-it74 and No. lzujbky-2017-it75], the International Cooperation Project of Ministry of Science and Technology [No. 2013DFA11140], and the Program of Beijing Municipal Science & Technology Commission [No. Z171100000117005].

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  • Xiaowei Li
    • 1
  • Rong La
    • 1
    Email author
  • Ying Wang
    • 1
  • Junhong Niu
    • 1
  • Shuai Zeng
    • 1
  • Shuting Sun
    • 1
  • Jing Zhu
    • 1
  1. 1.Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and EngineeringLanzhou UniversityLanzhouChina

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