  In universal environment, a patient-friendly inexpensive method is needed to
realize the early diagnosis of depression, which is believed to be an effective
way to reduce the mortality of depression. The purpose of this study is only to
collect EEG signal from three electrodes Fp1, Fpz and Fp2, then the linear and
nonlinear features of EEG used to classify depression patients and healthy
controls. The EEG recordings were carried out on a group of 18 medication-free
depressive patients and 25 gender and age matched controls. In this paper, the
selected features include three linear (maximum, mean and center values of the
power) and three nonlinear features (correlation dimension, Renyi entropy and
C0 complexity). The accuracy and effectiveness of classification model between
depressive and control subjects were calculated using leave-one-out
cross-validation. The experimental results indicate that selected three channel
EEG and features can distinguish the subjects between depression and normal
beings, the classification accuracy is 72.25%. It is hoped that the performed
results can provide more choices for the early diagnosis of depression in a
universal environment.
