Epilepsy is a neurological disorder of different types characterized by recurrent of seizures which affects people of all ages. This paper presents visibility graph similarity as a nonlinear model to analyze the epilepsy EEG data from different brain region of healthy and patient subjects with epilepsy seizures. All EEG segments are mapped into a corresponding graph to obtain the corresponding degree of sequence for each segment, and then the difference between these degrees is constructed as a similarity between two segments. The results showed that seizure activity presented strongest nonlinear dynamic response in the form of similarity level decreasing from healthy subjects to patients. Results of other sets were found to be in agreement with our results.