**Abstract**

A new method for the diagnosis of Alzheimer's disease in the mild stage is presented according to combining the characteristics of electroencephalogram (EEG) signal and magnetic resonance imaging (MRI) images. Then, proper features of brain signals are extracted according to the nonlinear and chaotic nature of the brain such as Lyapunov exponent, correlation dimension, and entropy. These features combined with brain MRI images properties include medial temporal lobe atrophy (MTA), cerebrospinal fluid flow (CSF), gray matter (GM), index asymmetry (IA), and white matter (WM) to diagnose the disease. Then two classifiers, the support vector machine and Elman neural network, are used with the optimal combined features extracted by analysis of variance. Results showed that between the three brain signals, and between the four modes of evaluation, the accuracy of the Pz channel and excitation mode was more than the others The accuracy of the results in Elman neural network with the combination of brain signal features and medical images is 94.4% and in the case without combining the signal and image features, the accuracy of the results is 92.2%.

**Keywords:** EEG, MRI, Alzheimer's diseases, SVM, Elman neural network
