Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep

Microsleep is often known as unintended loss of attention and alertness within short period of time briefly between a second up to 30 seconds. Microsleep might be dangerous to vehicle driver especially for long-distance driver due to unawareness and loss of focus towards surrounding environment. Thu...

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Main Authors:	Norizam, Sulaiman, Goh, K. S., Mamunur, Rashid, M. S., Jadin, Mahfuzah, Mustafa, M. Z., Ibrahim, Fahmi, Samsuri
Format:	Conference or Workshop Item
Language:	English English
Published:	2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/27516/ http://umpir.ump.edu.my/id/eprint/27516/1/Offline%20LabVIEW-based%20EEG%20Signals%20Analysis.pdf http://umpir.ump.edu.my/id/eprint/27516/2/Offline%20LabVIEW-based%20EEG%20Signals%20Analysis1.pdf

id	ump-27516
recordtype	eprints
spelling	ump-275162020-01-20T01:27:31Z http://umpir.ump.edu.my/id/eprint/27516/ Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep Norizam, Sulaiman Goh, K. S. Mamunur, Rashid M. S., Jadin Mahfuzah, Mustafa M. Z., Ibrahim Fahmi, Samsuri TK Electrical engineering. Electronics Nuclear engineering Microsleep is often known as unintended loss of attention and alertness within short period of time briefly between a second up to 30 seconds. Microsleep might be dangerous to vehicle driver especially for long-distance driver due to unawareness and loss of focus towards surrounding environment. Thus, microsleep detection system based on Electroencephalogram (EEG) signals is proposed in this research to prevent the drivers to involve in the accidents. For investigation purpose, six samples are chosen to obtain their brain signals using NeuroSky Mindwave Mobile Headset and eegID mobile application in two different states which are relax state for 5 minutes and driving state for 1 hour. Besides, a Graphical User Interface (GUI) is constructed using LabVIEW to analyze the EEG signals. The captured EEG signals then, are undergone pre-processing to remove noises and undesired artifacts. Bandpass filter is then applied to brainwaves to split the signals into Alpha and Theta waves. The patterns of these waves are examined and analyzed using power spectrum technique to search for unique features that might relate to microsleep event. The kNN classifier is employed to classify the selected features in term of Standard Deviation (SD) and Spectral Centroid (SC). The best classification accuracy for SD and SC features are obtained at 82.83% and 77.65% respectively for 80:20 training-testing ratios. Besides, the analysis of EEG Alpha and Theta band using Short-Time Fourier Transform (STFT) technique able to localize the EEG signals to indicate the exact time of the microsleep occurrence. The alarm system and steering vibration motor are assembled and will be activated for any detection of microsleep event. 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27516/1/Offline%20LabVIEW-based%20EEG%20Signals%20Analysis.pdf pdf en http://umpir.ump.edu.my/id/eprint/27516/2/Offline%20LabVIEW-based%20EEG%20Signals%20Analysis1.pdf Norizam, Sulaiman and Goh, K. S. and Mamunur, Rashid and M. S., Jadin and Mahfuzah, Mustafa and M. Z., Ibrahim and Fahmi, Samsuri (2019) Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep. In: 6th International Conference on Movement, Health and Exercise (MoHE) and 12th International Sports Science Conference (ISSC) 2019, 30 September-2 October 2019 , Kuching, Malaysia. pp. 1-21.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Norizam, Sulaiman Goh, K. S. Mamunur, Rashid M. S., Jadin Mahfuzah, Mustafa M. Z., Ibrahim Fahmi, Samsuri Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep
description	Microsleep is often known as unintended loss of attention and alertness within short period of time briefly between a second up to 30 seconds. Microsleep might be dangerous to vehicle driver especially for long-distance driver due to unawareness and loss of focus towards surrounding environment. Thus, microsleep detection system based on Electroencephalogram (EEG) signals is proposed in this research to prevent the drivers to involve in the accidents. For investigation purpose, six samples are chosen to obtain their brain signals using NeuroSky Mindwave Mobile Headset and eegID mobile application in two different states which are relax state for 5 minutes and driving state for 1 hour. Besides, a Graphical User Interface (GUI) is constructed using LabVIEW to analyze the EEG signals. The captured EEG signals then, are undergone pre-processing to remove noises and undesired artifacts. Bandpass filter is then applied to brainwaves to split the signals into Alpha and Theta waves. The patterns of these waves are examined and analyzed using power spectrum technique to search for unique features that might relate to microsleep event. The kNN classifier is employed to classify the selected features in term of Standard Deviation (SD) and Spectral Centroid (SC). The best classification accuracy for SD and SC features are obtained at 82.83% and 77.65% respectively for 80:20 training-testing ratios. Besides, the analysis of EEG Alpha and Theta band using Short-Time Fourier Transform (STFT) technique able to localize the EEG signals to indicate the exact time of the microsleep occurrence. The alarm system and steering vibration motor are assembled and will be activated for any detection of microsleep event.
format	Conference or Workshop Item
author	Norizam, Sulaiman Goh, K. S. Mamunur, Rashid M. S., Jadin Mahfuzah, Mustafa M. Z., Ibrahim Fahmi, Samsuri
author_facet	Norizam, Sulaiman Goh, K. S. Mamunur, Rashid M. S., Jadin Mahfuzah, Mustafa M. Z., Ibrahim Fahmi, Samsuri
author_sort	Norizam, Sulaiman
title	Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep
title_short	Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep
title_full	Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep
title_fullStr	Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep
title_full_unstemmed	Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep
title_sort	offline labview-based eeg signals analysis to detect vehicle driver microsleep
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/27516/ http://umpir.ump.edu.my/id/eprint/27516/1/Offline%20LabVIEW-based%20EEG%20Signals%20Analysis.pdf http://umpir.ump.edu.my/id/eprint/27516/2/Offline%20LabVIEW-based%20EEG%20Signals%20Analysis1.pdf
first_indexed	2023-09-18T22:43:17Z
last_indexed	2023-09-18T22:43:17Z
_version_	1777417057831223296

Offline LabVIEW-based EEG Signals Analysis to Detect Vehicle Driver Microsleep

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