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Deep Learning for Predicting Drivers Mental States from Electroencephalography (EEG).

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning for Predicting Drivers Mental States from Electroencephalography (EEG)./
Author:	Hajinoroozi, Mehdi.
Description:	1 online resource (127 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
Contained By:	Dissertation Abstracts International79-05B(E).
Subject:	Engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355533019

Deep Learning for Predicting Drivers Mental States from Electroencephalography (EEG).
Hajinoroozi, Mehdi.

Deep Learning for Predicting Drivers Mental States from Electroencephalography (EEG). - 1 online resource (127 pages)

Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2017.

Includes bibliographical references

The prediction and classification of the fatigue related mental states (drowsy/alert) of the drivers from electroencephalography (EEG) signals by means of deep learning methods and models is the concentration of this work. EEG data collected form drivers can be used for drivers' mental states (alert/drowsy states) prediction. The prediction of the drowsy and alert states of the drivers can be done by traditional machine learning methods but due to low performance of the traditional machine learning methods, it is desirable to examine new methods. Deep learning has already shown to be very successful in prediction of the complicated signals, especially in image processing field. In this work it is shown that different variation of deep learning models can be developed to effectively classify complicated EEG signals collected form drivers. In this work several novel variations of deep learning models have been developed and proposed for EGG data, related to drivers cognitive states, classifications which are able to considerably improve the predictions in comparison with traditional machine learning and custom deep learning models like convolutional neural networks. The developed and proposed deep learning models in this work can be eventually used in practical brain computer interface (BCI) systems which use EEG signals for brain and computer interactions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355533019Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Deep Learning for Predicting Drivers Mental States from Electroencephalography (EEG).
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500 $a Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
500 $a Adviser: Yufei Huang.
502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2017.
504 $a Includes bibliographical references
520 $a The prediction and classification of the fatigue related mental states (drowsy/alert) of the drivers from electroencephalography (EEG) signals by means of deep learning methods and models is the concentration of this work. EEG data collected form drivers can be used for drivers' mental states (alert/drowsy states) prediction. The prediction of the drowsy and alert states of the drivers can be done by traditional machine learning methods but due to low performance of the traditional machine learning methods, it is desirable to examine new methods. Deep learning has already shown to be very successful in prediction of the complicated signals, especially in image processing field. In this work it is shown that different variation of deep learning models can be developed to effectively classify complicated EEG signals collected form drivers. In this work several novel variations of deep learning models have been developed and proposed for EGG data, related to drivers cognitive states, classifications which are able to considerably improve the predictions in comparison with traditional machine learning and custom deep learning models like convolutional neural networks. The developed and proposed deep learning models in this work can be eventually used in practical brain computer interface (BCI) systems which use EEG signals for brain and computer interactions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0537
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Texas at San Antonio. $b Electrical & Computer Engineering. $3 845615
773 0 $t Dissertation Abstracts International $g 79-05B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10639429 $z click for full text (PQDT)