EEG-Based Mild Depressive Detection Using Differential Evolution

Depression has become a serious disease that affects people's mental health. How to detect it promptly and accurately is a difficult task. Electroencephalogram can reflect the spontaneous biological potential signals in the cerebral cortex and is widely used in the prediction and diagnosis of depression. With electroencephalogram, the key and most difficult challenge is to find the brain regions and frequencies associated with depression, especially mild depression. At present, the most commonly used method is the combination of feature selection and classification algorithm for detection. However, the classification accuracy needs to be further improved. The differential evolution is a population-based adaptive global optimization algorithm. Due to its fast convergence and strong robustness, this paper uses it to optimize the extracted features to achieve better result. Then the k-nearest neighbor classification algorithm is used to classify patients with mild depression and normal people. The experiment is performed on a data set of 10 subjects with mild depression and 10 normal subjects. The results show that the method can find the relatively optimal features and distinguish the two groups of subjects better. It effectively improves the classification accuracy and efficiency, and is superior to other feature optimization methods.