Early detection and timely intervention of cognitive impairment are crucial to slow down the progress of the disease. The ElectroEncephaloGraphy （EEG） signal has become an important tool for the investigation of biomarkers of cognitive diseases due to its high temporal resolution and easy acquisition. Compared with the traditional biomarker recognition method， the machine learning method has higher accuracy and better stability for the recognition and classification of cognitive impairment based on EEG signals. Aiming at the relevant research literature on the recognition and classification of cognitive impairment based on EEG signals in the past three years， firstly， from the perspectives of five categories of EEG features commonly used in the recognition and classification of cognitive impairment， including time domain， frequency domain， combination of time and frequency domains， nonlinear dynamics， functional connectivity and brain network， more representative EEG features were found. Then， the currently commonly used classification methods based on machine learning and deep learning， such as Support Vector Machine （SVM）， Linear Discriminant Analysis （LDA）， K-Nearest Neighbor （KNN） and Artificial Neural Network （ANN）， as well as their performance were summarized. Finally， the current problems in different kinds of studies were analyzed， and the future research directions in this field were prospected， thereby providing reference for the follow-up research on the recognition and classification of cognitive impairment based on EEG signals.