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. 2022 Jul 13:13:850159.
doi: 10.3389/fpsyg.2022.850159. eCollection 2022.

Research on the Method of Depression Detection by Single-Channel Electroencephalography Sensor

Xue Lei  1 , Weidong Ji  2   3 , Jingzhou Guo  1 , Xiaoyue Wu  1 , Huilin Wang  4 , Lina Zhu  1 , Liang Chen  1
Affiliations

Research on the Method of Depression Detection by Single-Channel Electroencephalography Sensor

Xue Lei et al. Front Psychol. .

Abstract

Depression is a common mental health illness worldwide that affects our quality of life and ability to work. Although prior research has used EEG signals to increase the accuracy to identify depression, the rates of underdiagnosis remain high, and novel methods are required to identify depression. In this study, we built a model based on single-channel, dry-electrode EEG sensor technology to detect state depression, which measures the intensity of depressive feelings and cognitions at a particular time. To test the accuracy of our model, we compared the results of our model with other commonly used methods for depression diagnosis, including the PHQ-9, Hamilton Depression Rating Scale (HAM-D), and House-Tree-Person (HTP) drawing test, in three different studies. In study 1, we compared the results of our model with PHQ-9 in a sample of 158 senior high students. The results showed that the consistency rate of the two methods was 61.4%. In study 2, the results of our model were compared with HAM-D among 71 adults. We found that the consistency rate of state-depression identification by the two methods was 63.38% when a HAM-D score above 7 was considered depression, while the consistency rate increased to 83.10% when subjects showed at least one depressive symptom (including depressed mood, guilt, suicide, lack of interest, retardation). In study 3, 68 adults participated in the study, and the results revealed that the consistency rate of our model and HTP drawing test was 91.2%. The results showed that our model is an effective means to identify state depression. Our study demonstrates that using our model, people with state depression could be identified in a timely manner and receive interventions or treatments, which may be helpful for the early detection of depression.

Keywords: EEG sensor; HAM-D; PHQ-9; depression detection; house-tree-person (HTP) drawing test; state depression.

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Conflict of interest statement

HW was employed by the Shanghai Fujia Cultural Development Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
“Attention” (the orange line) and “meditation” (the blue line) signals during psychological counseling intervention for depression of Subject A1. The red circle indicates the markers of state depression.
FIGURE 2
FIGURE 2
“Attention” (the orange line) and “meditation” (the blue line) signals in the HTP drawing test for subject A3 who did not have state depression.
FIGURE 3
FIGURE 3
A picture drawn by subject A3 who did not have state depression.
FIGURE 4
FIGURE 4
“Attention” (the orange line) and “meditation” (the blue line) signals in the HTP drawing test for subject A4 who had state depression. The red arrow indicates the markers of state depression.
FIGURE 5
FIGURE 5
A picture drawn by subject A4 who had state depression.
See this image and copyright information in PMC

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