**1. Introduction**

Diagnosis of the pain is one of the main concerns in clinical treatments procedure. In particular, detection of chronic or acute stage of the pain could be vital in the situations that oral information is not available for example instant for neonates, disabled persons, anesthetized patients and also animals. Multiple research projects have been done to originate and classify the pain. It is shown that achieving consistent is a challenge.

Identification of the human sensory perception have been of high interest in recent years. These studies are required for protection of the body and for restoring the

embodiment sense. The advance in this field shows that not only the accurate design of the sensors improves the sensitivity of the identification but understanding the dynamics of pain perception and successful reversing of the coding mechanism are essential stages of the processing and classification process.

Localization of the source of the pain is very important for the neurological therapeutic processes [1]. The localization of cortical sources and observation of the spatiotemporal activation is also used for pre-treatment monitoring and surgical process [2]. The studies in this area would create an infrastructure for real-time monitoring of the pain to be used in alarming systems, surgery monitors and automated activated systems.

The aim of this work is to show the relation of EEG signal and perceptual level of pain. We also try to clarify the relation between the signal and the origin of the pain. The alignment of electrodes in cortical regions on scalp are assigned based on an evolutional algorithm to improve the clinical monitoring results. The normal and pain conditions are used for recording the signal. Some defined spectral features are combined with non-linear features including approximate entropy and Lyapunov exponent to create the feature vector. It is shown that there is consistency between these features and the dynamical characteristic of EEG signals. Evolutionary optimization method is used for reduction of the features space dimension and computational costs. A hybrid adaptive network fuzzy inference system (ANFIS) and support vector machine (SVM) scheme is used as the classifier. ANFIS optimizer is used for alignment of kernels of SVM. The classification results show that pain levels could be differentiated with high accuracy, sensitivity, and specificity with few recording electrodes. This research shows that electrical variations of brain patterns could be used for determination of pain levels. The proposed classification method reaches an accuracy of 95%.
