||EEG-EOG Sensing Devices for Human-Computer Interaction and Sleep Analysis
||Institute of Medical Informatics
人的一生中睡眠佔據了三分之一的時間，擁有良好的睡眠品質能改善專注力、記憶力、及代謝功能。但並非所有人都能夠擁有良好的睡眠品質，對於睡眠疾病纏身的病患，需要至特定醫療機構使用多通道生理記錄儀(Polysomnography, PSG) 記錄整夜睡眠生理訊號，量測腦電訊號、眼動訊號、肌電訊號作為專家判讀睡眠階段的依據。PSG擁有強大且多樣的生理記錄功能，但記錄使用的大量電極導線已經造成使用者睡眠干擾因素之一也需要專業人員協助操作。相比與腦電訊號(EEG)量測的不便利性，眼動訊號(EOG)量測位置點只需要在眼睛周圍並不會有頭部濃密毛髮阻擋，更可以在此位置量測到腦電訊號(EEG)作為專家睡眠判讀的重要訊號。
因此本論文開發出一套使用眼罩構型以腦眼訊號為基礎的感測裝置，硬體上使用低雜訊類比前端(AFE)設計訊號擷取電路，配合內建藍芽的系統單晶片(SoC)做無線與有線通訊處理，為了驗證系統的穩定性、正確性、便利性、及應用性我們安排了三項實驗，實驗一: 驗證有線與無線傳輸訊號正確性並記錄眼罩穿戴與PSG設置時間比較兩者便利性;實驗二: 本系統與PSG同時收錄11位健康成年人的整夜睡眠EEG、眼罩EOG訊號，分析五階段睡眠訊號相關性與專家睡眠階段判讀的一致性，並以居家使用為目的連續收錄整晚睡眠與午睡驗證其穩定性;實驗三:基於眼罩構型實作一即時眼動方位偵測演算法應用於人機互動遊戲實現其應用性。睡眠專家判讀本系統量測EEG與眼罩EOG與PSG訊號所得到的專家睡眠階段判讀結果一致性皆有八成五以上，已到達目前臨床判讀的標準，此外相比於PSG平均四十七分鐘的設置時間，本系統穿戴只需兩分鐘有效縮短應用設置時間，達到即戴即用之目的。基於眼動的人機互動遊戲上，能有效在0.377(標準差 0.043)秒內辨識眼動方位且準確率達96%(標準差5.6)。以上證實我們的系統確實可以在睡眠訊號量測與人機互動應用上有良好的表現。
Humans spend one-third of their lifetimes on sleep. One with good quality of sleep can improve his or her attention, memories and metabolism. However, not all of the humans can have good quality of sleep. For those people who have been plagued with sleep disorders should use a multi-channel polysomnography (PSG) to improve them at some selected hospitals. The PSG records whole-night sleep physiological signals and the measurement of brain activity (EEG), eye movements (EOG) and muscle activity (EMG) as the parameters for the experts to undertake the research for sleep stage scoring. The PSG has enormous and multiple biophysiological recording functions. During sleep recording, attaching a large number of electrodes leads to subjects has become one of the sleep disturbances to them, which often requires extra help from technicians. Compared with EEG, EOG uses electrodes placed around the eyes without blocked by thick hair on the forehead, where EEG signals are relatively accessible to be measured for sleep scoring.
Therefore, we invented a set of eye-mask sensing device based on brain signals. In terms of hard drive device, we used low noise analog front-end (AFE) to design signals and harvest circuits in cooperation with a built-in Bluetooth of system on chip (SoC) for wired and wireless communication. In order to verify the convenience, accuracy, stability and applicability of this system, we conducted four types of experiments in this study. In Experiment 1, we collected 24 recording times of wearing eye-masks and 10 recording ones of setting up PSG to compare the two systems concerning their convenience. In Experiment 2, this system and PSG collected whole-night sleep recordings of both EEG and EOG signals on 11 healthy adults. This experiment attempts to prove that the signals are relevant and consistent with sleep staging scoring. In Experiment 3, for the purpose of daily uses, we collected recordings for 4 consecutive whole-night sleeps and 8 napping recordings to implement its stability. In Experiment 4, simultaneous eye movement detection algorithm was well applied to human-computer interaction games based on the structure of eye mask.
According to the results of the three previous experiments, we suggested that this system and PSG have acquired 85% agreement with sleep scoring reaching up to the standard of clinical judgment at present. Furthermore, in comparison to PSG setting time of 47 minutes on average, this system enabled the subjects to spend only two minutes wearing it. Its wearable and convenient features were proven to be true on reducing time for its set up. For the research on human-computer interaction games, it was also carried out to detect eye movements in 0.377 seconds (standard deviation: 0.043) reaching up to 96% accuracy (standard deviation: 5.6). As discussed above, this system is expected to have significant effect on the measurement of sleep signals and human-computer interaction.
List of Figures VII
List of Tables IX
Chapter 1 Introduction 1
1.1 Significance of Sleep 1
1.2 Related Works 2
1.2.1 Wearable devices 2
1.2.2 Sleep-Monitoring Devices 2
1.2.3 Comparing Sleep-Monitoring Devices 5
1.3 Motivation and Objective 7
1.4 Thesis Overview 8
Chapter 2 System Design and Implementation 9
2.1 System Architecture 9
2.2 Hardware Design and Implementation 10
2.2.1 Electrooculogram (EOG) 10
2.2.2 Eye-mask design 11
2.2.3 Analog Front End Circuit (AFE) 12
2.2.4 Accelerometer 13
2.2.5 Microcontroller Power Supply Circuit 15
2.2.6 AFE Power Supply Circuit 15
2.2.7 Specifications of Hardware Implementation 16
2.3 Firmware Implementation 17
2.3.1 Data Acquisition 19
2.3.2 Data Storage 19
2.3.3 Wireless Data Transmission 19
2.3.1 Wireless Data Format 20
Chapter 3 System and Signals Verification 21
3.1 System Verification 21
3.1.1 Wired Communication 21
3.1.2 Wireless Communication 22
3.1.3 Evaluation of System Settings Convenience 24
3.2 Evaluation of Signals Recording 26
3.2.1 Signal Pre-processing 26
3.2.2 Evaluation of EEG Signals 27
3.2.3 Evaluation of EOG Signals 33
3.2.4 Home Use Evaluation 36
3.3 Real Time Application 38
3.3.1 Eye Movement Detection Method 39
3.3.2 Game User Interface 41
3.3.3 Subjects and Recording 43
3.3.4 Results 43
Chapter 4 Discussion 45
Chapter 5 Conclusions 49
Chapter 6 Appendix 51
6.1 Sleep EEG pattern 51
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