博碩士論文 109522135 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:13 、訪客IP:3.141.193.158
姓名 潘奕平(Yi-Ping Pan)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 用特徵選擇減少疲勞偵測腦電圖通道數
(Channel Reduction for EEG Fatigue Detection Using Pearson’s Correlation and Mutual Information)
相關論文
★ 條件判斷式事件驅動程式設計之C語言擴充★ 基于小波变换的指纹活度检测,具有聚集 LPQ 和 LBP 特征
★ 應用自動化測試於異質環境機器學習管道之 MLOps 系統★ 設計具有可視化思維工具和程式作為單一步的 輔助學習程式之棋盤式遊戲
★ TOCTOU 漏洞的靜態分析與實作★ 用於繪製風力發電控制邏輯之特定領域語言
★ 在Java程式語言中以雙向結構表達數學公式間關聯之設計與實作★ 支援模組化規則製作之程式碼轉換工具
★ 基於替代語意的 pandas DataFrame 靜態型別檢查器★ 自動化時間複雜度分析的設計與實作–從軟體層面評估嵌入式系統的功率消耗
★ 以震波層析成像為應用之特定領域語言實作與分析★ 一個應用紙本運算與數位化於程式設計學習使程序性思維可視化的機制
★ 基於抽象語法樹的陣列形狀錯誤偵測★ 從合作學習角色分工獲得函式程式設計思維學習遞迴程式的機制
★ 基於抽象語法樹的深度複製及彈性別名之所有權系統解決 Java 表示暴露問題★ 基於 Python 型別提示檢查不可變性
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-7-1以後開放)
摘要(中) 疲勞駕駛在全球造成大量的傷亡。以臺灣為例,疲勞駕駛是佔所有意外的 20%。我 們定義了甚麼是疲勞,並且在相關研究中找到疲勞影響駕駛表現的證據。車道偏移量上 升、方向盤操控能力下降、反應時間變長、油門煞車控制(速度控制)變差,都是疲勞 駕駛會造成的。我們發現駕駛人本人很難認知道自己已經發生疲勞駕駛,因此需要透過 疲勞偵測系統輔助。我們分析了四種常見的疲勞駕駛系統及它們的優缺點,並且發現使 用 EEG 偵測疲勞是最精準且客觀的。可惜的是 EEG 目前無法使用在日常生活中,因為它使用非常麻煩且昂貴。我們的目標是能夠用把用降低腦電圖偵測疲勞的通道數,我們即可使腦電圖疲勞偵測落實在生活中。

本篇論文我們先是分析各種 EEG 分析方法、特徵選擇的方法。我們並接著研究篩 選器的架構: SVM 與 LSTM 的架構。我們的方法主要是盡可能的減少 40 個 EEG 通道 之間的關聯程度,並且利用篩選方法搭配上 LSTM 的機器分類方法,以達成減少通道 數。我們的結果,呼應我們的假設。在皮爾森的線性相關係數演算法中,我們使用 7 個 通道便達成 94.9 % 的疲勞準確度。而在相互資訊的演算法中,我們使用 6 個通道便達成 94.04 % 的疲勞準確度。

我們比較我們篩選出來的通道數,和當代 fMRI 觀察疲勞對於腦袋中的影響,兩者 皆顯示疲勞跟大腦的運動皮層有關。我們也和當代不同腦電圖特徵選擇應用方法比較, 並顯示我們的方法不論是在準確度還是減少的通道數都是比大多數的研究優秀。
摘要(英) Driver fatigue is an underestimated factor that leads to traffic accidents. This research reviewed researches and found out how drowsiness will impact driving performance in various ways. Increasing lane drifting, decreasing steering wheel controlling ability, increasing reaction time (RT), and poor speed control, are the signs and indicators for drivers to discover they are not suitable to operate the vehicle. We then reviewed the four most common ways to detect drowsiness. Various drowsiness detection methods this research reviewed, drowsiness detection via EEG has the highest accuracy. However, due to the multi-channel nature of the EEG signal, using EEG detection in everyday life nowadays is not possible. Our objective is to reduce the channel needed so that we could make EEG drowsiness detection useful in daily life.

First, this research looks into different ways to analyze EEG signal. This research inspects two feature selection methods, filter method and wrapper method. We also discussed two classification model, which is the widely used support vector machine (SVM) and the long short term model (LSTM). Our method introduced the feature selection method into drowsiness detection with the key idea of reducing the redundancies between 40 EEG channels with the combination of filter method and LSTM classifier. The result is that we obtain the accuracy of 94.9 % in 7 channel Pearson’s correlation method and 94.04 % in 6 channel mutual information method.

This research then evaluates the selected channel with some drowsiness research, which suggests that when it comes to drowsiness, this research should look into one’s motor cortex of the brain. This research compares our result to some state-of-the-art method in different EEG applications which show great results in both accuracy and the amount of channel reduced.
關鍵字(中) ★ 疲勞偵測
★ 特徵選擇
★ 腦電圖
★ 長短期記憶模型
★ 遞歸神經網路
★ 通道減少
關鍵字(英) ★ Feature Selection
★ EEG
★ fatigue detection
★ drowsiness detection
★ LSTM
論文目次 Contents
page
摘要 ix
Abstract xi
Contents xiii
List of Figures xv
Glossary xvii
1 Introduction 1
1.1 How drowsiness affects vehicle control........................................ 2
1.2 Recent ways to detect drowsiness............................................. 5
1.2.1 Drivers’ Operation Behavior Based Detection ........................ 5
1.2.2 Vehicle State ........................................................... 6
1.2.3 Drivers’ Physiological Behavior Based Detection ..................... 7
1.2.4 Driver’s Physiological Signal .......................................... 8
1.3 Motivation..................................................................... 9
1.4 Structure ...................................................................... 10
2 Related Work 13
2.1 Ways of EEG Analysis........................................................ 13
2.1.1 Frequency domain analysis ............................................ 13
2.1.2 Time domain analysis ................................................. 14
2.1.3 Time-frequency domain analysis ...................................... 15
2.1.4 Artificial neural network (ANN) analysis ............................. 16
2.2 Feature Selection on EEG signals............................................. 16
2.3 Classification Models.......................................................... 18
2.3.1 Support Vector Machine (SVM) ...................................... 19
2.3.2 Long Short-Term Memory Networks (LSTMs) ....................... 21
xiii
CONTENTS
3 Dataset & Methodology 25
3.1 Dataset ........................................................................ 25
3.2 Methodology .................................................................. 26
3.2.1 Preprocessing .......................................................... 26
3.2.2 Feature selection....................................................... 28
3.2.3 Classification Model: LSTM........................................... 29
4 Implementation 31
4.1 Prepossessing phase ........................................................... 31
4.2 Data analyzing phase ......................................................... 33
4.3 Feature selection phase........................................................ 34
4.4 Classification phase ........................................................... 35
5 Evaluation 37
5.1 Evaluate the practicability of proposed methodology ........................ 37
5.2 Accuracy comparison of previous work ....................................... 42
5.3 Application selected channel comparison of previous work .................. 44
6 Conclusion 47
Bibliography 49
參考文獻 [1] Williamson A M and Feyer A M “Moderate sleep deprivation produces impairments
in cognitive and motor performance equivalent to legally prescribed levels of alcohol
intoxication.”Occupational and environmental medicine vol. 57,10 (2000): 649-55.
doi:10.1136/oem.57.10.649
[2] Dawson D and K. Reid.“Fatigue, alcohol and performance impairment.”Nature vol.
388,6639 (1997): 235. doi:10.1038/40775
[3] Howard Mark E,“The interactive effects of extended wakefulness and low-dose alcohol
on simulated driving and vigilance.”Sleep vol. 30,10 (2007): 1334-40. doi:10.1093/sleep/
30.10.1334
[4] Katrin Vitols and Eckhard Voss, ”DRIVER FATIGUE IN EUROPEAN ROAD
TRANSPORT”, European Transport Workers’Federation (ETF)
[5] J. Connor, “The role of driver sleepiness in car crashes: a systematic review of
epidemiological studies.”Accident; analysis and prevention vol. 33,1 (2001): 31-41.
doi:10.1016/s0001-4575(00)00013-0
[6] Paul Jackson, Cassie Hilditch, Alex Holmes, Nick Reed, Natasha Merat and L. Smith,
”Fatigue and road safety: a critical analysis of recent evidence.”
[7] S. N. Biggs, A. Smith, J. Dorrian,K. Reid, D. Dawson, C. van den Heuvel and S.
Baulk, ”Perception of simulated driving performance after sleep restriction and caffeine.”, Journal of Psychosomatic Research, 63(6), 573–577. https://doi.org/10.1016/
j.jpsychores.2007.06.017
[8] Sarah Otmani, Thierry Pebayle, Joceline Roge and Alain Muzet, ”Effect of driving duration and partial sleep deprivation on subsequent alertness and performance
of car drivers.”, Physiology behavior, 84(5), 715–724. https://doi.org/10.1016/
j.physbeh.2005.02.021
49
BIBLIOGRAPHY
[9] S. D. Baulk, S. N. Biggs, K. J. Reid, C. J. van den Heuvel and D. Dawson, ”Chasing
the silver bullet: measuring driver fatigue using simple and complex tasks. Accident;
analysis and prevention”, 40(1), 396–402. https://doi.org/10.1016/j.aap.2007.07.008
2008;40(1):396-402. doi: 10.1016/j.aap.2007.07.008.
[10] Pierre Thiffault and Jacques Bergeron. ” Fatigue and individual differences in
monotonous simulated driving. Personality and Individual Differences.” 159-176.
10.1016/S0191-8869(02)00119-8.
[11] N. B. Powell, K. B. Schechtman, R. W. Riley, K. Li, R. Troell and C. Guilleminault. ”
The road to danger: the comparative risks of driving while sleepy.”, The Laryngoscope,
111(5), 887–893. https://doi.org/10.1097/00005537-200105000-00024
[12] L. Wang, X. Wu, and M. Yu, “Review of driver fatigue/drowsiness detection methods,”Journal of Biomedical Engineering, vol. 24, no. 1, pp. 245–248, 2007.
[13] J. Krajewski, D. Sommer , U. Trutschel, D. Edward and M. Golz, “Steering Wheel
Behavior Based Estimation of Fatigue”, Driving Assesment Conference. 5(2009). doi:
https://doi.org/10.17077/drivingassessment.1311
[14] Z. Li, L. Chen, L. Nie and S. X. Yang, ”A Novel Learning Model of Driver Fatigue
Features Representation for Steering Wheel Angle,” in IEEE Transactions on Vehicular
Technology, vol. 71, no. 1, pp. 269-281, Jan. 2022, doi: 10.1109/TVT.2021.3130152.
[15] W. Liying, ”The Design of the Steering Wheel with Anti-fatigue Driving for Vehicles Based on Pattern Recognition,” 2012 Fifth International Conference on Intelligent Computation Technology and Automation, 2012, pp. 340-343, doi: 10.1109/
ICICTA.2012.91.
[16] A. Eskandarian and A. Mortazavi, ”Evaluation of a Smart Algorithm for Commercial
Vehicle Driver Drowsiness Detection,” 2007 IEEE Intelligent Vehicles Symposium, 2007,
pp. 553-559, doi: 10.1109/IVS.2007.4290173.
[17] W. Zhang and Q. Fan, ”Identification of abnormal driving state based on driver’s
model,” ICCAS 2010, 2010, pp. 14-18, doi: 10.1109/ICCAS.2010.5669943.
[18] S. Arefnezhad, A. Eichberger, M. Frühwirth, C. Kaufmann and M. Moser, ”Driver
Drowsiness Classification Using Data Fusion of Vehicle-based Measures and ECG Signals,” 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC),
2020, pp. 451-456, doi: 10.1109/SMC42975.2020.9282867.
50
[19] Zutao Zhang and Jia-shu Zhang, ”Driver Fatigue Detection Based Intelligent Vehicle
Control,” 18th International Conference on Pattern Recognition (ICPR’06), 2006, pp.
1262-1265, doi: 10.1109/ICPR.2006.462.
[20] Parsai, R., Bajaj, P. (2007). Intelligent Monitoring System for Driver’s Alertness (A Vision Based Approach). In: Apolloni, B., Howlett, R.J., Jain, L. (eds)
Knowledge-Based Intelligent Information and Engineering Systems. KES 2007.
Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://
doi.org/ 10.1007/978-3-540-74819-958M.ErikssonandN.P.P apanikotopoulos, ”Eye −
trackingfordetectionof driverf atigue, ”P roceedingsofConferenceonIntelligentT ransportationS319, doi : 10.1109/IT SC.1997.660494.
[21][21] Chu Jiangwei, Jin Lisheng, Tong Bingliang, Shi Shuming and Wang Rongben, ”A
monitoring method of driver mouth behavior based on machine vision,” IEEE Intelligent
Vehicles Symposium, 2004, 2004, pp. 351-356, doi: 10.1109/IVS.2004.1336408.
[22] M. Simon, E. A. Schmidt, W. E. Kincses, M. Fritzsche, A. Bruns, C. Aufmuth, M.
Bogdan, W. Rosenstiel and M. Schrauf, ”EEG alpha spindle measures as indicators of
driver fatigue under real traffic conditions”, Clinical neurophysiology : official journal of
the International Federation of Clinical Neurophysiology, 122(6), 1168–1178. https://
doi.org/10.1016/j.clinph.2010.10.044
[23] Ruey-Song Huang, Tzyy-Ping Jung and Scott Makeig, ”Tonic Changes in EEG Power
Spectra during Simulated Driving”, FAC 2009: Foundations of Augmented Cognition.
Neuroergonomics and Operational Neuroscience pp 394–403
[24] B.T. Jap, S. Lal, P. Fischer, Bekiaris, “Using EEG spectral components to assess
algorithms for detecting fatigue,”Expert Systems with Applications, vol. 36, no. 2, pp.
2352–2359, 2009.
[25] Difei Jing, Dong Liu, Shuwei Zhang, Zhongyin Guo, ”Fatigue driving detection method
based on EEG analysis in low-voltage and hypoxia plateau environment”
[26] S. K. Lai, A. Craig, ”Driver fatigue: electroencephalography and psychological assessment.”, Psychophysiology, 39(3), 313–321. https://doi.org/10.1017/s0048577201393095
[27] B.T. Jap, S. Lal, P. Fischer, Bekiaris, “Using EEG spectral components to assess
algorithms for detecting fatigue,”Expert Systems with Applications, vol. 36, no. 2, pp.
2352–2359, 2009.
51
BIBLIOGRAPHY
[28] S. Wu, L. Gao, L. Wang, ”Detecting driving fatigue based on electroencephalogram”,
Trans. Beijing Inst. Technol., 12 (2009), p. 8
[29] E. A. Vivaldi and A. Bassi, ”Frequency Domain Analysis of Sleep EEG for Visualization
and Automated State Detection,” 2006 International Conference of the IEEE Engineering
in Medicine and Biology Society, 2006, pp. 3740-3743, doi: 10.1109/IEMBS.2006.259546.
[30] Z. Lin and Z. Huang, ”Research on event-related potentials in motor imagery BCI,”
2017 10th International Congress on Image and Signal Processing, BioMedical Engineering
and Informatics (CISP-BMEI), 2017, pp. 1-6, doi: 10.1109/CISP-BMEI.2017.8302267.
[31] D. Gabor, ”Theory of Communication”, J. Inst. Electr. Engrs., 1946, vol. 93 (pg. 429-
457)
[32] I. Daubechies, ”Ten Lectures on Wavelets”, 1992 Philadelphia, PaSociety for Industrial
and Applied Mathematicspg. 357
[33] Combes, J., Grossmann, A., Tchamitchian, P., ”Wavelets : time-frequency methods
and phase space”, proceedings of the international conference, Marseille, France, December 14-18, 1987.
[34] S. G. Mallat, ”A theory for multiresolution signal decomposition: the wavelet representation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no.
7, pp. 674-693, July 1989, doi: 10.1109/34.192463.
[35] Lyons RG. , ”Understanding Digital Signal Processing , 20042nd ed. Upper Saddle
River, NJPrentice Hall PTRpg. 688
[36] S. G. Mallat and Zhifeng Zhang, ”Matching pursuits with time-frequency dictionaries,”
in IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397-3415, Dec. 1993, doi:
10.1109/78.258082.
[37] G.B. Folland, A. Sitaram, ”The uncertainty principle: a mathematical survey”, J
Fourier Anal Appl, 1997, vol. 3 (pg. 207-233)
[38] Brian J. Roach, Daniel H. Mathalon, ”Event-Related EEG Time-Frequency Analysis:
An Overview of Measures and An Analysis of Early Gamma Band Phase Locking in
Schizophrenia”, Schizophrenia Bulletin, Volume 34, Issue 5, September 2008, Pages 907–
926, https://doi.org/10.1093/schbul/sbn093
52
[39] I. Belakhdar, W. Kaaniche, R. Djmel and B. Ouni, ”A comparison between ANN and
SVM classifier for drowsiness detection based on single EEG channel,” 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP),
2016, pp. 443-446, doi: 10.1109/ATSIP.2016.7523132.
[40] Robert S. Fisher, Walter van Emde Boas, Warren Blume, Christian Elger, Pierre Genton, Phillip Lee,Jerome Engel Jr., ”Epileptic Seizures and Epilepsy: Definitions Proposed
by the International League Against Epilepsy (ILAE) and the International Bureau for
Epilepsy (IBE)”
[41] Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik, ”A training algorithm
for optimal margin classifiers.”, In Proceedings of the fifth annual workshop on Computational learning theory (COLT ’92). Association for Computing Machinery, New York,
NY, USA, 144–152. https://doi.org/10.1145/130385.130401
[42] Corinna Cortes, Vladimir N. Vapnik (1995). ”Support-vector networks” . Machine
Learning. 20 (3): 273–297. CiteSeerX 10.1.1.15.9362. doi:10.1007/BF00994018. S2CID
206787478.
[43] Z. Liu, X. Lv, K. Liu and S. Shi, ”Study on SVM Compared with the other Text
Classification Methods,” 2010 Second International Workshop on Education Technology
and Computer Science, 2010, pp. 219-222, doi: 10.1109/ETCS.2010.248.
[44] Jianliang Min, Ping Wang, Jianfeng Hu. ”Driver fatigue detection through multiple
entropy fusion analysis in an EEG-based system.” December 8, 2017
[45] SD Faul, W Marnane, ”Dynamic, location-based channel selection for power consumption reduction in EEG analysis”. Comput. Methods Programs Biomed. 108, 1206–1215
(2012)
[46] K. Theresia Diah, A. Faqih and B. Kusumoputro, ”Exploring the Feature Selection
of the EEG Signal Time and Frequency Domain Features for k - NN and Weighted kNN,” 2019 IEEE R10 Humanitarian Technology Conference (R10-HTC)(47129), 2019,
pp. 196-199, doi: 10.1109/R10-HTC47129.2019.9042448.
[47] M. Lokman, A. Dabag, N. Ozkurt, S. Miqdad and M. Najeeb, ”Feature Selection and
Classification of EEG Finger Movement Based on Genetic Algorithm,” 2018 Innovations
in Intelligent Systems and Applications Conference (ASYU), 2018, pp. 1-5, doi: 10.1109/
ASYU.2018.8554029.
53
BIBLIOGRAPHY
[48] E. Shih, A. Shoeb, J. Guttag, Sensor selection for energy-efficient ambulatory medical
monitoring. Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, 2009
[49] E. L. Glassman and J. V. Guttag, ”Reducing the Number of Channels for an Ambulatory Patient-Specific EEG-based Epileptic Seizure Detector by Applying Recursive
Feature Elimination,” 2006 International Conference of the IEEE Engineering in Medicine
and Biology Society, 2006, pp. 2175-2178, doi: 10.1109/IEMBS.2006.260180.
[50] Ş. Bekiryazici, A. Demir and G. Yilmaz, ”Feature Selection and Analysis EEG Signals
with Sequential Forward Selection Algorithm and Different Classifiers,” 2020 28th Signal Processing and Communications Applications Conference (SIU), 2020, pp. 1-4, doi:
10.1109/SIU49456.2020.9302482.
[51] J.D. Henriksen, T.W. Kjaer, R.E. Madsen, L.S. Remvig, C.E. Thomsen, H.B. Sorensen,
”Channel selection for automatic seizure detection.”, Clin. Neurophysiol. 123(1), 84–92
(2012)
[52] N. Jatupaiboon, S. Pan-ngum, P. Israsena, Emotion classification using minimal EEG
channels and frequency bands. Proceedings of 10th Int’l Joint conf. on Computer Science
[53] T. Cao, F. Wan , C.M. Wong, J.N. da Cruz , Y. Hu, ”Objective evaluation of fatigue by
EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces.” Biomedical engineering online. 2014; 13:1. https://doi.org/10.1186/1475-925X-13-1
[54] Xiong Y, Gao J, Yang Y, Yu X, Huang W., ”Classifying Driving Fatigue Based on
Combined Entropy Measure Using EEG Signals.”, International Journal of Control and
Automation. 2016; 9(3):329–38.
[55] D. Garrett, D. A. Peterson, C. W. Anderson and M. H. Thaut, ”Comparison of linear,
nonlinear, and feature selection methods for EEG signal classification,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 141-144, June
2003, doi: 10.1109/TNSRE.2003.814441.
[56] Thomas W. Parks and C. Sidney S. Burrus. ”Digital Filter Design.”, Topics in Digital
Signal Processing. Wiley, New York, 1987. ISBN 978-0-471-82896-9.
[57] Emmanuel C. Ifeachor and Barrie W. Jervis., ”Digital Signal Processing: A Practical
Approach.”, Pearson, 2 edition, 2002.
54
[58] Aapo Hyvarinen, Juha Karhunen, and Erkki Oja.“Independent Component Analysis”
[59] Congedo, Marco; Gouy-Pailler, Cedric; Jutten, Christian (December 2008). ”On the
blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics”. Clinical Neurophysiology. 119 (12): 2677–2686.
arXiv:0812.0494. doi:10.1016/j.clinph.2008.09.007. PMID 18993114
[60] Blum and Langley, 1997; Dash and Liu, 1997; Kohavi and John, 1997
[61] Noelia Sánchez-Maroño, Amparo Alonso-Betanzos María Tombilla-Sanromán, ”Filter
Methods for Feature Selection –A Comparative Study”, DOI: 10.1007/978-3-540-77226-
2-19
[62] S. Hochreiter and J. Schmidhuber, “Long short-term memory,”Neural Comput., vol.
9, no. 8, pp. 1735–1780, Nov. 1997.
[63] F. A. Gers, J. Schmidhuber, and F. Cummins,“Learning to forget: Continual prediction
with LSTM,”in Proc. 9th Int. Conf. Artif. Neural Netw. ICANN, 1999, pp. 850–855.
[64] F. A. Gers, N. N. Schraudolph, and J. Schmidhuber, “Learning precise timing with
LSTM recurrent networks,”J. Mach. Learn. Res., vol. 3, pp. 115–143, Aug. 2002
[65] Patrick S. Hogan, Steven X. Chen, Wen Wen Teh, Vikram S. Chib. , ”Neural mechanisms underlying the effects of physical fatigue on effort-based choice”, Nature Communications, 2020; 11 (1) DOI: 10.1038/s41467-020-17855-5
[66] A.G. Correa, L. Orosco, E. Laciar, ”Automatic detection of drowsiness in EEG records
based on multimodal analysis”, Medical Engineering Physics. 2014; 36(2):244–9.
[67] Y. Xiong, J. Gao, Y. Yang, X. Yu, W. Huang, ”Classifying Driving Fatigue Based on
Combined Entropy Measure Using EEG Signals”, International Journal of Control and
Automation. 2016; 9(3):329–38.
[68] Rifai Chai, Ganesh R Naik, Tuan Nghia Nguyen, Sai Ho Ling, Yvonne Tran, Ashley Craig, Hung T Nguyen, ”Driver fatigue classification with independent component
by entropy rate bound minimization analysis in an EEG-based system.”, IEEE Journal
of Biomedical and Health Informatics. 2016;PP(99):1–, https://doi.org/10.1109/JBHI
2016.2532354.
55
BIBLIOGRAPHY
[69] C. Zhang, H. Wang and R. Fu, ”Automated Detection of Driver Fatigue Based on
Entropy and Complexity Measures,” in IEEE Transactions on Intelligent Transportation
Systems, vol. 15, no. 1, pp. 168-177, Feb. 2014, doi: 10.1109/TITS.2013.2275192.
[70] Jinghai Yin, Jianfeng Hu, Zhendong Mu, ”Developing and evaluating a mobile driver
fatigue detection network based on electroencephalograph signals”, Healthcare Technology
Letters. 2016:pp.1–5, https://doi.org/10.1049/htl.2016.0013
[71] Li-Wei Ko et al., ”Single channel wireless EEG device for real-time fatigue level detection,” 2015 International Joint Conference on Neural Networks (IJCNN), 2015, pp. 1-5,
doi: 10.1109/IJCNN.2015.7280817.
[72] Y. Wang, X. Liu, Y. Zhang, Z. Zhu, D. Liu and J. Sun, ”Driving Fatigue Detection Based on EEG Signal,” 2015 Fifth International Conference on Instrumentation and
Measurement, Computer, Communication and Control (IMCCC), 2015, pp. 715-718, doi:
10.1109/IMCCC.2015.156.
[73] Chang, Nibin, C G Wen and Y L Chen. “Mobile Healthcare System for Driver Based
on Drowsy Detection Using EEG Signal Analysis.”(2015).
[74] Nugraha, Brilian Tafjira, Riyanarto Sarno, Dimas Anton Asfani, Tomohiko Igasaki and
Muhammad Nadzeri Munawar.“Classification of driver fatigue state based on EEG using
Emotiv EPOC.”Journal of theoretical and applied information technology 86 (2016): 347-
359.
[75] Alotaiby et al. EURASIP Journal on Advances in Signal Processing (2015) 2015:66 DOI
10.1186/s13634-015-0251-9
[76] Luck SJ (2005). An Introduction to the Event-Related Potential Technique. The MIT
Press. ISBN 978-0-262-12277-1.
[77] A. Piryatinska, W.A. Woyczynski, M.S. Scher, K.A. Loparo, ”Optimal channel selection
for analysis of EEG-sleep patterns of neonates”, J. Comput. Methods Programs Biomed.
106(1), 14–26 (2012)
[78] E. Shih, A. Shoeb, J. Guttag, Sensor selection for energy-efficient ambulatory medical
monitoring. Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, 2009
56
[79] A. Temko, G. Lightbody, G. Boylan and W. Marnane, ”Online EEG channel weighting
for detection of seizures in the neonate,” 2011 Annual International Conference of the
IEEE Engineering in Medicine and Biology Society, 2011, pp. 1447-1450, doi: 10.1109/
IEMBS.2011.6090358.
[80] H. Tekgul, B.F.D. Bourgeois, K. Gauvreau, A.M. Bergin, ”Electroencephalography in
neonatal seizures: comparison of a reduced and a full 10/20 montage.”, Pediatr. Neurol.
32(3), 155–161 (2005)
[81] K. M. Ong, K. H. Thung, C. Y. Wee, R. Paramesranle, ”Selection of a subset of EEG
channels using PCA to classify alcoholics and non-alcoholics.”, Proceedings of the 2005
IEEE Engineering in Medicine and Biology 27th
[82] T. Lan, D. Erdogmus, A. Adami, S. Mathan, M. Pavel, ”Channel selection and feature projection for cognitive load estimation using ambulatory EEG.”, Computational
Intelligence and Neuroscience (2007)
[83] M. Li, J. Ma and S. Jia, ”Optimal combination of channels selection based on common
spatial pattern algorithm,” 2011 IEEE International Conference on Mechatronics and
Automation, 2011, pp. 295-300, doi: 10.1109/ICMA.2011.5985673.
[84] Henar Mike O. Canilang, Ej Miguel Francisco C. Caliwag, Judith Njoku Nyechinyere,
Angela C. Caliwag and Wansu Lim, ”Edge EEG: Edge AI Device-based EEG Signal
Processing for Emotion Recognition”
[85] Y. Huang, K. Wang, Y. Ho, C. He and W. Fang, ”An Edge AI System-on-Chip Design
with Customized Convolutional-Neural-Network Architecture for Real-time EEG-Based
Affective Computing System,” 2019 IEEE Biomedical Circuits and Systems Conference
(BioCAS), 2019, pp. 1-4, doi: 10.1109/BIOCAS.2019.8919038.
ystem,” 2019 IEEE Biomedical Circuits and Systems Conference
[86] https://github.com/ncu-psl/DrosinessDetectionEEG
指導教授 莊永裕(Yung-Yu Zhuang) 審核日期 2022-7-19
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明