Abstract: Computerised electroencephalography (EEG) is one of a wide variety of brain imaging techniques used in addiction medicine. It is a sensitive measure of the effects of addiction on the brain and has been shown to show changes in brain electrical activity during addiction. But, the clinical value of computerised EEG recording in addictions is not yet clearly established. However, several studies argue that this non-invasive technique has an undeniable contribution to the understanding, prediction, diagnosis and monitoring of addictions. The aim of this article is to assess, through a systematic review, the contribution and interest of computerised EEG in the study and understanding of substance abuse by describing the different electrical activities that underlie it across the main frequency ranges: delta, theta, alpha, beta and gamma. We have been conducting a systematic review according to the recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and the Cochrane Group. We included 25 studies with a total of 1897 cases of addiction and 1504 controls. The studies dealt with addictions related to 05 licit and illicit psychoactive substances (alcohol, nicotine, cannabis, heroin and cocaine). The group of addicted patients showed significantly different brain electrical characteristics from the group of controls in the different EEG rhythms, whether during acute substance intoxication, abuse, withdrawal, abstinence, relapse, progression or response to treatment. The majority of studies have used EEG for diagnostic, predictive, monitoring purposes and also to discover electro-physiological markers of certain addictions.

Aziz Mengad, Jamal Dirkaoui, Merouane Ertel, Meryem Chakkouch and Fatima Elomari, “The Contribution of Numerical EEG Analysis for the Study and Understanding of Addictions with Substances” International Journal of Advanced Computer Science and Applications(IJACSA), 14(5), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140534

@article{Mengad2023,
title = {The Contribution of Numerical EEG Analysis for the Study and Understanding of Addictions with Substances},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140534},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140534},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {5},
author = {Aziz Mengad and Jamal Dirkaoui and Merouane Ertel and Meryem Chakkouch and Fatima Elomari}
}