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Subtractive fuzzy classifier based driver distraction levels classification using EEG

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Main Authors: Wali, Mousa Kadhim, Murugappan, M., Dr., R. Badlishah, Ahmad, Prof. Dr.
Other Authors: musawali@yahoo.com
Format: Article
Published: Society of Physical Therapy Science 2014
Subjects:
Discrete wavelet transform
EEG
Fuzzy inference system
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/34610
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spelling my.unimap-346102014-05-21T23:24:42Z Subtractive fuzzy classifier based driver distraction levels classification using EEG Wali, Mousa Kadhim Murugappan, M., Dr. R. Badlishah, Ahmad, Prof. Dr. musawali@yahoo.com murugappan@unimap.edu.my badli@unimap.edu.my Discrete wavelet transform EEG Fuzzy inference system Link to publisher's homepage at https://www.jstage.jst.go.jp/ [Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects and Methods] Fifty Asian subjects (n=50, 43 males, 7 females), age range 20-35 years, who were free from any disease, participated in this study. Wireless EEG signals were recorded by 14 electrodes during four types of distraction stimuli (Global Position Systems (GPS), music player, short message service (SMS), and mental tasks). We derived the amplitude spectrum of three different frequency bands, theta, alpha, and beta of EEG. Then, based on fusion of discrete wavelet packet transforms and fast fourier transform yield, we extracted two features (power spectral density, spectral centroid frequency) of different wavelets (db4, db8, sym8, and coif5). Mean ± SD was calculated and analysis of variance (ANOVA) was performed. A fuzzy inference system classifier was applied to different wavelets using the two extracted features. [Results] The results indicate that the two features of sym8 posses highly significant discrimination across the four levels of distraction, and the best average accuracy achieved by the subtractive fuzzy classifier was 79.21% using the power spectral density feature extracted using the sym8 wavelet. [Conclusion] These findings suggest that EEG signals can be used to monitor distraction level intensity in order to alert drivers to high levels of distraction. 2014-05-21T23:24:42Z 2014-05-21T23:24:42Z 2013 Article Journal of Physical Therapy Science, vol. 25(9), 2013, pages 1055-1058 2187-5626 (Online) 0915-5287 (Print) https://www.jstage.jst.go.jp/article/jpts/25/9/25_jpts-2013-099/_article http://dspace.unimap.edu.my:80/dspace/handle/123456789/34610 http://dx.doi.org/10.1589/jpts.25.1055 Society of Physical Therapy Science
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/
topic Discrete wavelet transform
EEG
Fuzzy inference system
spellingShingle Discrete wavelet transform
EEG
Fuzzy inference system
Wali, Mousa Kadhim
Murugappan, M., Dr.
R. Badlishah, Ahmad, Prof. Dr.
Subtractive fuzzy classifier based driver distraction levels classification using EEG
description Link to publisher's homepage at https://www.jstage.jst.go.jp/
author2 musawali@yahoo.com
author_facet musawali@yahoo.com
Wali, Mousa Kadhim
Murugappan, M., Dr.
R. Badlishah, Ahmad, Prof. Dr.
format Article
author Wali, Mousa Kadhim
Murugappan, M., Dr.
R. Badlishah, Ahmad, Prof. Dr.
author_sort Wali, Mousa Kadhim
title Subtractive fuzzy classifier based driver distraction levels classification using EEG
title_short Subtractive fuzzy classifier based driver distraction levels classification using EEG
title_full Subtractive fuzzy classifier based driver distraction levels classification using EEG
title_fullStr Subtractive fuzzy classifier based driver distraction levels classification using EEG
title_full_unstemmed Subtractive fuzzy classifier based driver distraction levels classification using EEG
title_sort subtractive fuzzy classifier based driver distraction levels classification using eeg
publisher Society of Physical Therapy Science
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/34610
_version_ 1643797541759746048
score 13.211869

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