Citation
Mahmoud, Elsayed and Sim, Kok Swee and Tan, Shing Chiang (2023) The Pain Signals: A Systematic Review on the Electroencephalogram of the Nociceptive Pain. Engineering Letters, 31 (4). pp. 1759-1769. ISSN 1816-093X
Text
53.pdf - Published Version Restricted to Repository staff only Download (1MB) |
Abstract
Since the birth of modern medicine and neuroscience, scientists have been searching for a pain centre in the brain. In particular, they have been trying to find a pain biomarker in the electrical activity of the human brain. This search was not only motivated by mere curiosity but also by an immense need in medicine. Finding a brain electrical indicator or biomarker to objectively measure the sensation of pain is vital in medical practice and the pharmacological development of pain remedies. Furthermore, it has recently been observed that transient painful stimuli activate several brain parts with electrical patterns. This has prompted researchers to pursue a quest to objectively measure nociceptive pain based on biological biomarkers. In this paper, we review research in the literature that attempted to identify physical pain from a specific brain activity or correlate pain with any variations in brain rhythms. Even though a comprehensive understanding of the nature and effects of pain remains unavailable, general trends have been observed in the literature. Based on our survey, most researchers agreed on the correlation between the sensation of pain and two electrical activities: (i) an increase in Gamma power in the frontal cortex and (ii) various electrical activities in the primary somatosensory cortex (e.g., a decrease in Alpha power). Another research trend that was observed is the use of machine learning for classifying different intensities of pain-related.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Human Brain |
Subjects: | Q Science > QM Human anatomy |
Divisions: | Faculty of Engineering and Technology (FET) Faculty of Information Science and Technology (FIST) |
Depositing User: | Ms Nurul Iqtiani Ahmad |
Date Deposited: | 04 Jan 2024 01:28 |
Last Modified: | 04 Jan 2024 01:28 |
URII: | http://shdl.mmu.edu.my/id/eprint/12018 |
Downloads
Downloads per month over past year
Edit (login required) |