Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environ...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dulan Perera [verfasserIn] Yu-Kai Wang [verfasserIn] Chin-Teng Lin [verfasserIn] Hung Nguyen [verfasserIn] Rifai Chai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	distracted driving brain connectivity GGC DTF PDC GPDC

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 16, p 6230
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:16, p 6230

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22166230

Katalog-ID:	DOAJ079206522

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allfields	10.3390/s22166230 doi (DE-627)DOAJ079206522 (DE-599)DOAJe9c118c44ad54971ab59acc5abaabafe DE-627 ger DE-627 rakwb eng TP1-1185 Dulan Perera verfasserin aut Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions. distracted driving brain connectivity GGC DTF PDC GPDC Chemical technology Yu-Kai Wang verfasserin aut Chin-Teng Lin verfasserin aut Hung Nguyen verfasserin aut Rifai Chai verfasserin aut In Sensors MDPI AG, 2003 22(2022), 16, p 6230 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:16, p 6230 https://doi.org/10.3390/s22166230 kostenfrei https://doaj.org/article/e9c118c44ad54971ab59acc5abaabafe kostenfrei https://www.mdpi.com/1424-8220/22/16/6230 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 16, p 6230
spelling	10.3390/s22166230 doi (DE-627)DOAJ079206522 (DE-599)DOAJe9c118c44ad54971ab59acc5abaabafe DE-627 ger DE-627 rakwb eng TP1-1185 Dulan Perera verfasserin aut Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions. distracted driving brain connectivity GGC DTF PDC GPDC Chemical technology Yu-Kai Wang verfasserin aut Chin-Teng Lin verfasserin aut Hung Nguyen verfasserin aut Rifai Chai verfasserin aut In Sensors MDPI AG, 2003 22(2022), 16, p 6230 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:16, p 6230 https://doi.org/10.3390/s22166230 kostenfrei https://doaj.org/article/e9c118c44ad54971ab59acc5abaabafe kostenfrei https://www.mdpi.com/1424-8220/22/16/6230 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 16, p 6230
allfields_unstemmed	10.3390/s22166230 doi (DE-627)DOAJ079206522 (DE-599)DOAJe9c118c44ad54971ab59acc5abaabafe DE-627 ger DE-627 rakwb eng TP1-1185 Dulan Perera verfasserin aut Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions. distracted driving brain connectivity GGC DTF PDC GPDC Chemical technology Yu-Kai Wang verfasserin aut Chin-Teng Lin verfasserin aut Hung Nguyen verfasserin aut Rifai Chai verfasserin aut In Sensors MDPI AG, 2003 22(2022), 16, p 6230 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:16, p 6230 https://doi.org/10.3390/s22166230 kostenfrei https://doaj.org/article/e9c118c44ad54971ab59acc5abaabafe kostenfrei https://www.mdpi.com/1424-8220/22/16/6230 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 16, p 6230
allfieldsGer	10.3390/s22166230 doi (DE-627)DOAJ079206522 (DE-599)DOAJe9c118c44ad54971ab59acc5abaabafe DE-627 ger DE-627 rakwb eng TP1-1185 Dulan Perera verfasserin aut Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions. distracted driving brain connectivity GGC DTF PDC GPDC Chemical technology Yu-Kai Wang verfasserin aut Chin-Teng Lin verfasserin aut Hung Nguyen verfasserin aut Rifai Chai verfasserin aut In Sensors MDPI AG, 2003 22(2022), 16, p 6230 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:16, p 6230 https://doi.org/10.3390/s22166230 kostenfrei https://doaj.org/article/e9c118c44ad54971ab59acc5abaabafe kostenfrei https://www.mdpi.com/1424-8220/22/16/6230 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 16, p 6230
allfieldsSound	10.3390/s22166230 doi (DE-627)DOAJ079206522 (DE-599)DOAJe9c118c44ad54971ab59acc5abaabafe DE-627 ger DE-627 rakwb eng TP1-1185 Dulan Perera verfasserin aut Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions. distracted driving brain connectivity GGC DTF PDC GPDC Chemical technology Yu-Kai Wang verfasserin aut Chin-Teng Lin verfasserin aut Hung Nguyen verfasserin aut Rifai Chai verfasserin aut In Sensors MDPI AG, 2003 22(2022), 16, p 6230 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:16, p 6230 https://doi.org/10.3390/s22166230 kostenfrei https://doaj.org/article/e9c118c44ad54971ab59acc5abaabafe kostenfrei https://www.mdpi.com/1424-8220/22/16/6230 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 16, p 6230
language	English
source	In Sensors 22(2022), 16, p 6230 volume:22 year:2022 number:16, p 6230
sourceStr	In Sensors 22(2022), 16, p 6230 volume:22 year:2022 number:16, p 6230
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authorswithroles_txt_mv	Dulan Perera @@aut@@ Yu-Kai Wang @@aut@@ Chin-Teng Lin @@aut@@ Hung Nguyen @@aut@@ Rifai Chai @@aut@@
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callnumber-first	T - Technology
author	Dulan Perera
spellingShingle	Dulan Perera misc TP1-1185 misc distracted driving misc brain connectivity misc GGC misc DTF misc PDC misc GPDC misc Chemical technology Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators
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topic_title	TP1-1185 Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators distracted driving brain connectivity GGC DTF PDC GPDC
topic	misc TP1-1185 misc distracted driving misc brain connectivity misc GGC misc DTF misc PDC misc GPDC misc Chemical technology
topic_unstemmed	misc TP1-1185 misc distracted driving misc brain connectivity misc GGC misc DTF misc PDC misc GPDC misc Chemical technology
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title	Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators
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title_full	Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators
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title_sort	improving eeg-based driver distraction classification using brain connectivity estimators
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title_auth	Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators
abstract	This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.
abstractGer	This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.
abstract_unstemmed	This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.
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title_short	Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators
url	https://doi.org/10.3390/s22166230 https://doaj.org/article/e9c118c44ad54971ab59acc5abaabafe https://www.mdpi.com/1424-8220/22/16/6230 https://doaj.org/toc/1424-8220
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author2	Yu-Kai Wang Chin-Teng Lin Hung Nguyen Rifai Chai
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up_date	2024-07-03T22:16:24.850Z
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Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators

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