The early diagnosis has an very important role in curing dementia. But there was not the effective method to diagnose it until now. In this paper we analyzed the EEG of Alzheimer's disease patients and normal groups by nonlinear methods. In the analysis we calculated the correlation dimensions D₂and the largest Lyapunov exponent L₁. We found that patients with Alzheimer's disease have significantly lower D₂and TEX>$L_1$ than normal groups. It means that brains injured by Alzheimer's disease have electrophysiological inactive elements and have decreased chaotic behaviour. We propose the nonlinear analysis of the EEG as a useful tool for the early diagnosis of Alzheimer's disease.