Driver Mental States Monitoring Based on Brain Signals [Elektronische Ressource] / Shengguang Lei. Betreuer: Matthias Rötting
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Driver Mental States Monitoring Based on Brain Signals [Elektronische Ressource] / Shengguang Lei. Betreuer: Matthias Rötting

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Description

Driver Mental States Monitoring Based on Brain Signals vorgelegt von Master of Engineering Shengguang Lei aus Hunan, China Von der Fakultät V - Verkehrs- und Maschinensysteme der Technischen Universität Berlin zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften Dr. -Ing genehmigte Dissertation Promotionsausschuss: Vorsitzender: Prof. Dr. phil. Manfred Thüring Berichter: Prof. Dr. -Ing. Matthias Rötting rof. Dr. -Ing. Takashi Toriizuka Tag der wissenschaftlichen Aussprache: 19.7.2011 Berlin 2011 D 83 Acknowledgement First of all, I would like to express my sincere gratitude to my supervisor Prof. Dr.-Ing Matthias Rötting for his encouragement, guidance and continuous support of my Ph.D study. His patience, enthusiasm, and immense knowledge helped me in all the time of my research and writing of this dissertation. I would also like to thank Prof. Dr. Takashi Toriizuka and the rest of my thesis committee for their encouragement, insightful comments and questions. My sincere thanks also go to all of my colleagues in the Chair of Human- Machine-Systems for the stimulating discussions and the enjoyable time in the last four years. In particular, I would like to thank Mario Lasch and Stefan Damke for their untired help and support during the experiments.

Informations

Publié par
Publié le 01 janvier 2011
Nombre de lectures 26
Langue English
Poids de l'ouvrage 9 Mo

Extrait



Driver Mental States Monitoring Based on Brain Signals


vorgelegt von
Master of Engineering
Shengguang Lei
aus Hunan, China




Von der Fakultät V - Verkehrs- und Maschinensysteme
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
Dr. -Ing

genehmigte Dissertation




Promotionsausschuss:
Vorsitzender: Prof. Dr. phil. Manfred Thüring
Berichter: Prof. Dr. -Ing. Matthias Rötting rof. Dr. -Ing. Takashi Toriizuka

Tag der wissenschaftlichen Aussprache: 19.7.2011



Berlin 2011

D 83


Acknowledgement
First of all, I would like to express my sincere gratitude to my supervisor
Prof. Dr.-Ing Matthias Rötting for his encouragement, guidance and
continuous support of my Ph.D study. His patience, enthusiasm, and
immense knowledge helped me in all the time of my research and writing of
this dissertation. I would also like to thank Prof. Dr. Takashi Toriizuka and
the rest of my thesis committee for their encouragement, insightful
comments and questions.
My sincere thanks also go to all of my colleagues in the Chair of Human-
Machine-Systems for the stimulating discussions and the enjoyable time in
the last four years. In particular, I would like to thank Mario Lasch and
Stefan Damke for their untired help and support during the experiments.
Also I would like to thank my colleagues Sebastian Welke and Marco
Pedrotti for the collaboration in this project, and Micheal Beckman for
helping me with the German abstract translation.
Last but not the least, I am thankful to my wife, Mrs. Peng Cheng,
supporting me spiritually throughout my life. I would also like to thank my
family: my parents Yanghuai Lei and Baimei Liu, for giving birth to me, and
my parents-in-law Zhaoyi Cheng and Shufen Xing for their thoughtful care
of my life.




Contents

Summary............................................................................................................................. I
Zusammenfassung ............................................................................................................V
Chapter 1. Introduction.................................................................................................... 1 2. Theoretical Background................................................................................ 9
2.1 Adaptive task allocation.............................................................................................9
2.1.1 The concept of adaptive task allocation.........................................................9
2.1.2 Mental workload ...........................................................................................12
2.1.3 Task demand, workload, and performance.................................................18
2.1.4 The demand-workload-matched model for adaptive task allocation
(DWM-ATA) ...........................................................................................................23
2.2 The measurement of mental workload ...................................................................27
2.2.1 Subjective rating............................................................................................30
2.2.2 Performance measures..................................................................................32
2.2.3 Physiological measures35
2.3 Electrocardiogram (ECG) .......................................................................................42
2.3.1 ECG and ECG measures...............................................................................42
2.3.2 ECG as index of workload............................................................................44
2.4 Electroencephalogram (EEG) .................................................................................46
2.4.1 Mechanism of EEG generation: the brain as a bioelectric generator.......47
2.4.2 EEG measurement and parameters.............................................................48
2.4.3 EEG as an index of mental workload ..........................................................55
2.5 Psychophysiology-driven adaptive aiding design ..................................................62
2.6 Driving task and driver task load ...........................................................................65
2.6.1 Driving task and driver mental workload...................................................66
2.6.2 Neural correlates of driving..........................................................................70
2.6.3 State-of-the-art driver workload assessment using psychophysiological
signals ......................................................................................................................75
2.7 Limitations of the current EEG-workload research .............................................78
2.8 Summary of the theoretical background................................................................80
Chapter 3. Representation of driver of workload in EEG: ERP or Band Powers? .. 83
3.1 Motivation.................................................................................................................83
3.2 Introduction of the tasks..........................................................................................84
3.2.1 Lane Change Task .........................................................................................84
3.2.2 Paced Auditory Serial Addition Task (PASAT)...........................................86
3.3 Pre-study: Manipulating workload in Lane Change Task....................................86
i3.4 Assessment of driver’s mental workload with EEG..............................................89
3.4.1 Participants....................................................................................................89
3.4.2 Experiment apparatus ..................................................................................89
3.4.3 Experiment procedure91
3.4.4 Data analysis..................................................................................................91
3.5 Results .......................................................................................................................96
3.5.1 Task performance..........................................................................................96
3.5.2 ERP in LCT ...................................................................................................97
3.5.3 ERP and workload ........................................................................................99
3.5.4 Band Powers and workload........................................................................103
3.5.5 Classification accuracy ...............................................................................109
3.6 Discussion................................................................................................................ 110
3.6.1 What are these components in ERP: A Task Analysis.............................. 110
3.6.2 Effect of task load on the amplitude and latency of P300........................ 111
3.6.3 Effect ofon the EEG spectrum parameters 113
3.6.4 Which is robust for workload representation: ERPs or band powers?.. 116
3.7 Summary................................................................................................................. 119
Chapter 4. EEG spectrum modulation with task combination .................................121
4.1 Motivation...............................................................................................................121
4.2 Methods...................................................................................................................122
4.2.1 Participants..................................................................................................122
4.2.2 Experiment apparatus ................................................................................123
4.2.3 Tasks .............................................................................................................124
4.2.4 Procedures....................................................................................................125
4.2.5 Data analysis................................................................................................126
4.3 Results .....................................................................................................................128
4.3.1 Subjective load (NASA-TLX).....................................................................128
4.3.2 Task performance........................................................................................129
4.3.3 Heart rate and heart rate variability.........................................................132
4.3.4 General modulation of the EEG parameters ............................................133
4.3.5 Short-term modulation of the EEG.......................................136
4.3.6 Correlation of EEG parameters to other variables ..................................141
4.4 Discussion................................................................................................................142
4.4.1 Modulation of theta and alpha power with workload..............................142
4.4.2 Other v

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