Depression Is An Important Type Of Mood Disorder With Prominent, Prolonged, And Depressed Moods As The Main Clinical Features. 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
