臺灣博碩士論文加值系統

運動已是每個人日常生活中習慣之一，適當的運動大多帶來好的影響，但運動後所造成的疲勞也會有負面的情況發生，再加上近年來科技發達，人與機器的合作越來越密切，像是自駕車技術蓬勃發展，但是在駕駛者上的技術不是100%完善，而且大多文獻沒有探討運動後身體疲勞(physical fatigue)對駕駛使用自駕車的影響。因此，本論文以腦電波(Electroencephalogram, EEG)來探討在physical fatigue的狀態下，如何影響使用者發現並糾正 autonomy 犯錯所需要的時間。研究中使用虛擬實境技術之動態駕車裝置，來模擬真實之駕車環境。利用室內腳踏車(ergometer)來操弄身體疲勞。本研究招募十八位健康的受測者參加了自駕車實驗。來探討身體疲勞對於駕駛在專心自駕車任務與分心自駕車任務所造成腦電波上的反應變化。結果發現在額葉的區域，因為運動後疲勞造成 Delta頻帶 (0.1~3 Hz) 和 Alpha 頻帶 (8~12Hz) 的能量分別有顯著下降及上升，反映出運動後在感知上的低效處理，在頂葉區域，觀察到有與額葉同步的現象，說明著駕駛難以協調並處理外在干擾。特別是在額葉分心任務的Gamma 頻帶 (30~50Hz)的能量會因為數學問題而顯著下降，我們發現受試者大腦的神經活動會更傾向在數學的內心運算問題上。再與行為結果上一起相比，發現身體活動變慢，心理認知運算變快。此一實驗之結果發現運動後的疲勞會使駕駛更依賴自駕車並且傾向於做內在心理上的運算，本研究透過腦電圖，可提供自動駕駛製造商在未來發展上，提供對於人們使用自駕車的建議，設計適用於人類和技術的自動駕駛汽車。

Exercise is one of the habits in everyone's daily life. Appropriate sports mostly bring good effects, but the fatigue caused by exercise will also influence human negatively. In addition, the recent developments of technology result in, more close collaborations between human and intelligent agents, especially autonomous driving technology, However, this new driving technology is still not perfect. And there is no literature to discuss physical fatigue on autonomous driving cars' influences. An ergometer is used to manipulate the degree of physical fatigue. The virtual reality-based driving environment was built to simulate the real driving scenario in which the drivers were asked to monitor autonomous car, answers cognitive tasks (math calculation) and correct the error as autonomous fails. Eighteen healthy subjects participated in this experiment. To explore the effects of physical fatigue, heart rate, behavior, and Electroencephalogram (EEG) were recorded during semi-autonomous driving. The results of this study showed that the heart rate increased significantly after exercise. In behavior, the steering wheel of the physical activity became slower, but the inner calculation became faster (mathematical problem). In EEG, it was found that in the frontal lobe, the energy of the Delta band (0.1~3 Hz) and the Alpha band (8~12Hz) will change due to the fatigue caused by exercise, reflect the inefficient processing to inhibit the sensory interference after exercise. Especially in the Gamma band of the distracting task (30~50 Hz) will vary due to mathematical problems, indicated that the subject tried to interrupt neural activity that can interfere with the ongoing math problem-solving. In the parietal region, it is observed that there is a phenomenon of synchronization with the frontal lobe, indicating that it is difficult to coordinate and deal with external disturbances. The results indicated physical fatigue could cause humans to have inefficient cognitive function for external sensory processing and more focus on on-going internal mental activity (e.g., math question), which makes humans to rely more on the autonomy for car driving. This study uses EEG to provide suggestions for the future development of autonomous driving manufacturers for the use of people, to design the autonomous driving car that is also suitable for human and technical.

摘要 i
Abstract ii
Table of Contents iv
LIST OF FIGURES AND TABLE vi
Chapter 1 INTRODUCTION 1
1.1 Brain-Behavior Relationships in physical fatigue 1
1.2 The use of the autonomous driving vehicle 2
1.3 Previous Literature 3
1.4 Aims of This Study 7
Chapter 2 METHOD AND EXPERIMENTAL ENVIRONMENT 8
2.1 Dynamic Driving Environment 9
2.2 EEG Signal Acquisition 10
2.3 Experimental Design 11
2.4 Participants 18
2.5 Independent Component Analysis (ICA) 19
2.6 Time-Frequency Analysis 22
2.7 Statistics Method 24
Chapter 3 EXPERIMENTAL RESULTS 25
3.1 Comparing with Previous Literature 25
3.2 Behavioral Performance 26
3.3 Average heart rate 29
3.4 EEG brain dynamics ERSPs results 30
3.4.1 EEG of Frontal Region 31
3.4.2 EEG of parietal region 35
Chapter 4 DISCUSSION 39
4.1 Behavior 39
4.2 Brain dynamic 40
Chapter 5 CONCLUSIONS 42
REFERENCE 43
APPENDIX 47
EEG of Central Region 47
EEG of Occipital Region 48

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