The invention relates to a multi-lead correlation analysis electroencephalo-graph (EEG) feature extraction method. In multi-class motor imagery task recognition, EEG signal features of brain areas activated by a specific motor imagery task are effectively extracted, and effectively extracting the EEG signal features of the brain areas activated by the specific motor imagery task is a key problem to improve a recognition rate. With the multi-lead correlation analysis EEG feature extraction method, firstly multi-lead motor imagery EEG signals are extracted, then a correlation coefficient between every two lead EEG signals is analyzed to obtain a correlation parameter matrix, next a row variance of each correlation parameter matrix, the ratio values of the sum of all the row variances, and natural logarithms of all the row variances are calculated, obtained results are used as characteristic vectors of the EEG signals, and finally the characteristic vectors are input into a classifier to complete classifying recognition of multi-class motor imagery tasks. With the multi-lead correlation analysis EEG feature extraction method, not only can the EEG signal features of the brain areas activated by the specific motor imagery task at the same time can be fully extracted, influences on characteristic parameters can be reduced to a large extent, wherein the influences are caused by EEG signal individual differences, and further the problem that insufficience problem of electrode choosing can be solved.