**4. Result**

Selected optimal features by ANOVA method in four modes of the closed eye, open eye, Reminder and stimulation are shown in **Table 2** to compare the number and types of optimum features between Fz, Cz, Pz channels. The results of the accuracy of the separation of brain signals using SVM with different core functions with optimal characteristics obtained from ANOVA are shown in **Figure 8**. **Figure 9** evaluates closed-eye, open-eye, reminder, and stimulation modes for the desired channels. According to the four modes of closing the eyes, opening the eyes, reminding and stimulating the brain signal, in order to better identify and introduce the features more accurately, each section is considered with a certain index. The closed part is considered with index c, the open eye part is considered with index o, the reminder part is with index r, and the stimulation part is considered

*EEG and MRI Processing for Alzheimer's Diseases DOI: http://dx.doi.org/10.5772/intechopen.107162*

#### **Figure 9.**

*Evaluation of closed-eye, open-eye, reminder and excitation modes for Fz, Cz, Pz channels in ANOVA mode.*

#### **Figure 10.**

*Compares the results of the resolution of brain signals by the SVM classifier with different core functions with ANOVA-optimized features with MRI features and without MRI features.*

with index s. In the excitation section, the target and non-target sound sections are defined with st and ss indices, respectively. In **Figure 10**, the results of separation of brain signals by SVM classifier with different main functions are compared with optimized ANOVA features with MRI features and without MRI features.

Due to the comparison of the results in **Figure 11**, by the support vector machine with different cores by the optimal brain signal characteristics by ANOVA with the addition of MRI image features, the accuracy of the results is reduced compared to the case where only the brain signal features are used.

Finally, by using neural network dynamics because of the nonlinear properties studied and due to the nonlinear dynamics of the EEG signal, Elman neural network is used. The results in **Figure 12** are compared by Elman with the optimal features of the brain signal obtained from ANOVA and with the addition of the features of MRI images, and the accuracy of the results is increased compared to the case where only the features of the brain signal are used.

#### **Figure 11.**

*Support vector machine with different cores by the optimal brain signal characteristics by ANOVA with the addition of MRI image.*

#### **Figure 12.**

*Compared by Elman with the optimal features of the brain signal obtained from ANOVA and with the addition of the features of MRI images.*
