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Differentiate Characteristic EEG Tobacco Smoking and Nonsmoking

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Main Authors: Lim Chee, Chin, Asmiedah, Muhamad Zazid, Chong Yen, Fook, Vikneswaran, Vijean, Saidatul Ardeenawatie, Awang, Marwan, Affandi, Lim Sin, Che
Other Authors: cheechin10@gmail.com
Format: Article
Language:English
Published: IOP Publishing 2020
Subjects:
Electroencephalogram (EEG)
Smoking
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69032
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spelling my.unimap-690322020-12-16T08:34:28Z Differentiate Characteristic EEG Tobacco Smoking and Nonsmoking Lim Chee, Chin Asmiedah, Muhamad Zazid Chong Yen, Fook Vikneswaran, Vijean Saidatul Ardeenawatie, Awang Marwan, Affandi Lim Sin, Che cheechin10@gmail.com Electroencephalogram (EEG) Smoking Link to publisher's homepage at https://iopscience.iop.org/ Electroencephalogram (EEG) signal is non-stationary signal that have low frequency component and amplitude compared to stationary signal. Therefore, present of unwanted substance (nicotine) in Tobacco smoking will alter the brain electrical activity. This paper is proposed to investigate the changes of EEG signal with the present of nicotine and identify the difference brain signal between smoker and non-smoker. There are 20 males (10 smokers, 10 non-smokers) are selected. The subjects are chosen based on inclusion criteria (abstained from smoking within 6 hours before experiment, and do not take any medication and caffeine). The recorded EEG signal contain a lot of noise such as head moving, muscle movement, power line, eyes blinks and interference with other device. Butterworth filter are implemented to remove the unwanted noise present in the original signal. Bandpass filter is used to decompose the EEG signal into alpha, theta, delta and beta frequency. Then, eight features (mean, median, maximum, minimum, variance, standard deviation, energy and power) have been extracted by using Fast Fourier Transform (FFT) and Power Spectral Density (PSD) method. Then, four different type of kernel function (‘Linear’, 'BoxConstraint', ‘Polynomial’ and ‘RBF’) of SVM classifier are used to identify the best accuracy. As a result, PSD (97.50%) have higher performance accuracy than FFT (97.33%) by using Radial Basis Function (RBF) of Support Vector Machine (SVM). Smoking activity caused slightly increase theta and delta frequency. Smoking is activated of five electrode channels (Fp1, Fp2, F8, F3 and C3) and caused additional emotion such as deep rest, stress releasing and losing attention. The attention of smokers can be measure by using stroop test. After smoking activity, smokers become more energetic and increase the time response (1.77 s) of stroop test compared to non-smokers (2.96 s). The result is calculated by using statistical analysis (t-test). The p-value is 0.037 which is less than 0.05. Thus, the null hypothesis is rejected and conclude there is significant different between smokers and non-smoker performance before and after smoking task. 2020-12-16T08:34:28Z 2020-12-16T08:34:28Z 2019 Article Journal of Physics: Conference Series, vol.1372, 2019, 8 pages 1742-6588 (print) 1742-6596 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69032 https://iopscience.iop.org/issue/1742-6596/1372/1 en International Conference on Biomedical Engineering (ICoBE); IOP Publishing
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Electroencephalogram (EEG)
Smoking
spellingShingle Electroencephalogram (EEG)
Smoking
Lim Chee, Chin
Asmiedah, Muhamad Zazid
Chong Yen, Fook
Vikneswaran, Vijean
Saidatul Ardeenawatie, Awang
Marwan, Affandi
Lim Sin, Che
Differentiate Characteristic EEG Tobacco Smoking and Nonsmoking
description Link to publisher's homepage at https://iopscience.iop.org/
author2 cheechin10@gmail.com
author_facet cheechin10@gmail.com
Lim Chee, Chin
Asmiedah, Muhamad Zazid
Chong Yen, Fook
Vikneswaran, Vijean
Saidatul Ardeenawatie, Awang
Marwan, Affandi
Lim Sin, Che
format Article
author Lim Chee, Chin
Asmiedah, Muhamad Zazid
Chong Yen, Fook
Vikneswaran, Vijean
Saidatul Ardeenawatie, Awang
Marwan, Affandi
Lim Sin, Che
author_sort Lim Chee, Chin
title Differentiate Characteristic EEG Tobacco Smoking and Nonsmoking
title_short Differentiate Characteristic EEG Tobacco Smoking and Nonsmoking
title_full Differentiate Characteristic EEG Tobacco Smoking and Nonsmoking
title_fullStr Differentiate Characteristic EEG Tobacco Smoking and Nonsmoking
title_full_unstemmed Differentiate Characteristic EEG Tobacco Smoking and Nonsmoking
title_sort differentiate characteristic eeg tobacco smoking and nonsmoking
publisher IOP Publishing
publishDate 2020
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69032
_version_ 1698698546421694464
score 13.214268

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