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Wavelet packet transform based driver distraction level 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
Language:English
Published: Mathematical Problems in Engineering 2014
Subjects:
Discrete wavelet packet transforms
Driver distractions
Electroencephalogram signals
Fuzzy inference system
Global position systems
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/34549
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spelling my.unimap-345492016-06-12T14:13:12Z Wavelet packet transform based driver distraction level classification using EEG Wali, Mousa Kadhim Murugappan, M., Dr. R. Badlishah, Ahmad, Prof. Dr. musawali@yahoo.com murugappan@unimap.edu.my badi@unimap.edu.my Discrete wavelet packet transforms Driver distractions Electroencephalogram signals Fuzzy inference system Global position systems Link to publisher’s homepage at http://www.hindawi.com/ We classify the driver distraction level (neutral, low, medium, and high) based on different wavelets and classifiers using wireless electroencephalogram (EEG) signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS), music player, short message service (SMS), and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was based on fusion of discrete wavelet packet transform (DWPT) and FFT. Comparing the results of three different classifiers (subtractive fuzzy clustering probabilistic neural network, K -nearest neighbor) was based on spectral centroid, and power spectral features extracted by different wavelets (db4, db8, sym8, and coif5). The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test. 2014-05-20T07:18:20Z 2014-05-20T07:18:20Z 2013 Article Mathematical Problems in Engineering, vol. 2013(2013), pages 1-11 1024-123X (Print) 1563-5147 (Online) http://www.hindawi.com/journals/mpe/2013/297587/ http://dspace.unimap.edu.my:80/dspace/handle/123456789/34549 http://dx.doi.org/10.1155/2013/297587 en Mathematical Problems in Engineering
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 Discrete wavelet packet transforms
Driver distractions
Electroencephalogram signals
Fuzzy inference system
Global position systems
spellingShingle Discrete wavelet packet transforms
Driver distractions
Electroencephalogram signals
Fuzzy inference system
Global position systems
Wali, Mousa Kadhim
Murugappan, M., Dr.
R. Badlishah, Ahmad, Prof. Dr.
Wavelet packet transform based driver distraction level classification using EEG
description Link to publisher’s homepage at http://www.hindawi.com/
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 Wavelet packet transform based driver distraction level classification using EEG
title_short Wavelet packet transform based driver distraction level classification using EEG
title_full Wavelet packet transform based driver distraction level classification using EEG
title_fullStr Wavelet packet transform based driver distraction level classification using EEG
title_full_unstemmed Wavelet packet transform based driver distraction level classification using EEG
title_sort wavelet packet transform based driver distraction level classification using eeg
publisher Mathematical Problems in Engineering
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/34549
_version_ 1643797518579924992
score 13.222552

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