**5. Conclusion**

EEG exhibits nonlinearity and weak stationarity. Thus, nonlinear EEG analysis is useful to investigate the clinical application for epilepsy, as shown in studies using the public record of Andrzejak 2001. We reviewed the studies using this data set. Using a variety of feature extraction methods and classifier methods, we conclude that these studies played a valuable role in EEG diagnosis for epilepsy. Comorbid psychiatric disorders in patients with epilepsy have not been reported in nonlinear EEG analysis except for one case series of comorbid psychotic disorders. In the future, according to the evolution of artificial intelligence, deep learning, new nonlinear analysis, and the association with the rating scale of the quality of life and psychiatric symptoms, we anticipate that EEG diagnosis for epilepsy, seizures, and comorbid psychiatric disorders in patients with epilepsy will become possible.
