2D EEG Topography and Spectral Analysis for Monitoring Working Memory Status of Smokers

• Published in: Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020
• Main Author: Sulaiman M.S.S.; Mansor W.; Naushad S.N.A.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104840050&doi=10.1109%2fIECBES48179.2021.9398784&partnerID=40&md5=6583136cbfb057a56a7c0fe7482d2ec4

In the abstinence of nicotine, smokers have difficulty to stay focus, handle stress and keep productive. Monitoring working memory performance of smokers is essential during the therapy to cure addiction. The monitoring has not been studied using non-invasive EEG. This paper describes the 2D topography and spectral analysis of smokers' EEG signals to identify the working memory status. The purpose of the study was to select the suitable tasks to be utilized in the neurofeedback system for curing smoking addiction. The EEG signals were recorded from smokers during memory tasks before and after smoking. The delta and alpha band of the EEG signals were filtered and the average power spectral density was computed. The 2D EEG topography was then carried out to analyse the working memory state of each subject. It was found that the working memory of the smokers was more active after smoking and the tasks employed in the study could reveal the brain state of smokers. © 2021 IEEE.