Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis
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 activatio...
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| Format: | Article |
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Tehran University of Medical Sciences
2025-07-01
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| Series: | Frontiers in Biomedical Technologies |
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| Online Access: | https://fbt.tums.ac.ir/index.php/fbt/article/view/429 |
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| author | Mahsa Tavasoli Zahra Einalou Reza Akhondzadeh |
| author_facet | Mahsa Tavasoli Zahra Einalou Reza Akhondzadeh |
| author_sort | Mahsa Tavasoli |
| collection | DOAJ |
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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 features of the brain during happening pain. Finally, pain detection can improve and upgrade medical methods.
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| format | Article |
| id | doaj-art-e36a4f4c81764b6d9bff0208972302ae |
| institution | Kabale University |
| issn | 2345-5837 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Frontiers in Biomedical Technologies |
| spelling | doaj-art-e36a4f4c81764b6d9bff0208972302ae2025-08-20T04:02:32ZengTehran University of Medical SciencesFrontiers in Biomedical Technologies2345-58372025-07-0112310.18502/fbt.v12i3.19182Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA AnalysisMahsa Tavasoli0Zahra Einalou1Reza Akhondzadeh2Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, IranDepartment of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, IranDepartment of Anesthesiology, Pain Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 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 features 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/429Cold Pressor TestElectroencephalogramPhasic PainRough Neural NetworkRecurrence Quantification AnalysisElectroencephalogram Dynamics |
| spellingShingle | Mahsa Tavasoli Zahra Einalou Reza Akhondzadeh Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis Frontiers in Biomedical Technologies Cold Pressor Test Electroencephalogram Phasic Pain Rough Neural Network Recurrence Quantification Analysis Electroencephalogram Dynamics |
| title | Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis |
| title_full | Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis |
| title_fullStr | Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis |
| title_full_unstemmed | Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis |
| title_short | Decoding Pain Dynamics: EEG Insights into Neural Responses and Classification via RQA Analysis |
| title_sort | decoding pain dynamics eeg insights into neural responses and classification via rqa analysis |
| topic | Cold Pressor Test Electroencephalogram Phasic Pain Rough Neural Network Recurrence Quantification Analysis Electroencephalogram Dynamics |
| url | https://fbt.tums.ac.ir/index.php/fbt/article/view/429 |
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