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Low channel electroencephalogram based deep learning method to pre-screening depression

Major depressive disorder (MDD) is a serious disease that negatively affects mood, thought, and behavior, and in severe cases, even suicide. many studies have been conducted to diagnose depression based on physiological data such as EEG (electroencephalogram) signals. There are many studies conducted by measuring EEG signals with a professional multi-channel device for the diagnosis of depression, but it is difficult to apply this method in a low-channel EEG signals measurement environment that individuals can easily access. Therefore, this study proposes a deep learning method to detect depression in advance based on low-channel EEG signals. Four frontal channels were separated into delta, theta, alpha, and beta band frequencies and trained in a two-dimensional deep learning model based on a convolution layer. And, the result was 96.97% accuracy, 90.97% sensitivity, and 99.00% specificity.