Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 3 2025 07 01 571 586 Mahsa Tavasoli Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran Zahra Einalou Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran Reza Akhondzadeh Department of Anesthesiology, Pain Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 2021 11 12 2024 03 02 Purpose: Pain is an unpleasant sensation that is important in all therapeutic conditions. So far, some researches have been done on pain assessment and cognition, and researchers have come to evaluate pain through different tests and methods. Since the occurrence of pain causes along with activation of a long network in brain regions, so recognition of dynamical changes of the brain in pain states is helpful for pain detection using Electroencephalogram (EEG) signal. Materials and methods: The aim of this research is to investigate dynamical changes of the brain for pain detection using EEG at the time of happening phasic pain. For this purpose, at the first step phasic pain is produced using coldness, then dynamical features via EEG are analyzed via Recurrence Quantification Analysis (RQA) method and finally Rough neural network classifier has been used for achieving accuracy to detect and categorize pain and non-pain states. Results: The performance of the classification procedure is 95.25 4%. That is compared with other research, it is a novel method of using rough neural network for distinguishing pain from non-pain states. Conclusion: The simulation results proved that cerebral behaviors are detectable during pain. Also, one of the most merits of the proposed method is the high accuracy of classifier for an investigation into dynamical fatures of the brain during happening pain. Finally, pain detection can improve and upgrade medical methods.  https://fbt.tums.ac.ir/index.php/fbt/article/view/429 https://fbt.tums.ac.ir/index.php/fbt/article/download/429/512