**2. Literature review**

The study of human brain functionality in special conditions like stress and pain has significantly improved in the last decades [2]. Only some few changes in EEG signal have been observed during pain condition. An experimental pain stimulus will cause a decrease in alpha spectrum and an increase in gamma power in the surface of cortex while tonic muscle pain usually led to a stronger beta activity [3]. In most of the works a cold press has been mainly used to induce pain to the subjects. The achieved results are not necessarily consistent and do not allow for generalization of the events.

The staging of the signals has shown more progress regardless of the specificity of the described EEG changes for pain. The EEG spectrum is affected by sensory processing in general and cognitive sensory signals change during these events. The ambiguity of the effects of these events on EEG is probably a consequence of the methods used for EEG analysis, which do not allow for sufficient experimental control. In the present work a machine learning approach is used for classification and recognition of pain to be used for diagnosing purposes.

The aim of this research is to identify the difference between "normal", "low pain" and "pain" conditions. A kernel based SVM is used for the classification of the signals in the desired classes [4]. The optimized hyperplane is adjusted by finding the maximum distance from the nearest training points. An ANFIS optimizer is used for adjusting the hyper-planes of SVM classifier. ANFIS is trained by the features in the

data set and adjusts the system parameters according to the error criteria [5]. Our results show that the combination of ANFIS-SVM results to the best performance on nonlinear features.
