DOI: 10.14704/nq.2018.16.6.1539

Recognition Algorithm of Driving Fatigue Related Problems Based on EEG Signals

Yun Li

Abstract


In order to quickly and accurately identify the fatigue related phenomena such as the continuous attention level of the driver after long-time driving and his reaction time to emergencies, the present study establishes a recognition algorithm for recognition of driving- fatigue related problems based on EEG signal index combining the improved particle swarm algorithm with support vector machine, and sets up a method for classifying driving fatigue degree. According to the fatigue classification method, two hours are served as critical points to delineate mild fatigue and severe fatigue. The recognition rate of driver's severe fatigue by the algorithm is higher than that of driver's mild fatigue. The driver's fatigue perception (7.35) in the second phase is much greater than that of the first phase (2.11), and the average reaction time (640 ms) and speed deviation (3.8 km / h) of the second phase are also much greater than that of the first phase (510 ms) and (1.4 km / h), indicating that the driver experiences obvious fatigue after driving for two consecutive hours, and his ability to deal with emergencies and to control vehicle during severe fatigue decrease.

Keywords


EEG Signals, Driving Fatigue, Support Vector Machine, Continuous Attention Level, Reaction Time

Full Text:

PDF

References


Bonnefond M, Henst JBVD, Gougain M, Robic S, Olsen MD, Weiss O. How pragmatic interpretations arise from conditionals: profiling the affirmation of the consequent argument with reaction time and EEG measures. Journal of Memory & Language 2012; 67(4): 468-85.

Chuang CH, Huang CS, Ko LW, Lin CT. An EEG-based perceptual function integration network for application to drowsy driving. Knowledge-Based Systems 2015; 80(C): 143-52.

Chuang CH, KO LW, Jung TP, Lin CT. Kinesthesia in a sustained-attention driving task. Neuroimage 2014; 91(8): 187-202.

Fu JW, Li M, Lu BL. Detecting Drowsiness in Driving Simulation Based on EEG. Autonomous Systems – Self-Organization, Management, and Control 2008; Springer Netherlands.

Garcés CA, Orosco L, Laciar E. Automatic detection of drowsiness in EEG records based on multimodal analysis. Medical Engineering & Physics 2014; 36(2): 244-244.

Halmaoui H, Joulan K, HautièRe N, Cord A. Quantitative model of the driver's reaction time during daytime fog – application to a head up display-based advanced driver assistance system. IET Intelligent Transport Systems 2014; 9(4): 375-81.

Hu J. Automated detection of driver fatigue based on adaboost classifier with EEG signals. Frontiers in Computational Neuroscience 2017; 11: 72-72.

Iampetch S, Punsawad Y, Wongsawat Y. EEG-based mental fatigue prediction for driving application. Biomedical Engineering International Conference 2012: 1-5.

Jap BT, Lal S, Fischer P. Comparing combinations of EEG activity in train drivers during monotonous driving. Expert Systems with Applications 2011; 38(1): 996-1003.

Kar S, Bhagat M, Routray A. EEG signal analysis for the assessment and quantification of driver’s fatigue. Transportation Research Part F Traffic Psychology & Behavior 2010; 13(5): 297-306.

Kim IH, Kim JW, Haufe S, Lee SW. Detection of braking intention in diverse situations during simulated driving based on EEG feature combination. Journal of Neural Engineering 2015; 12(1): 016001-016001.

King LM, Nguyen HT, Lal SKL. Early Driver Fatigue Detection from Electroencephalography Signals using Artificial Neural Networks. Engineering in Medicine and Biology Society, 2006. EMBS '06. International Conference of the IEEE 2006; 1: 2187-90.

Lal SK, Craig A. A critical review of the psychophysiology of driver fatigue. Biological Psychology 2001; 55(3): 173-94.

Lal SK, Craig A. Driver fatigue: electroencephalography and psychological assessment. Psychophysiology 2002; 39(3): 313-21.

Lal SKL, Craig A. Electroencephalography activity associated with driver fatigue: implications for a fatigue countermeasure device. Journal of Psychophysiology 2001; 15(3): 183-89.

Lal SKL, Craig A, Boord P, Kirkup L, Nguyen H. Development of an algorithm for an EEG-based driver fatigue countermeasure. Journal of Safety Research 2003; 34(3): 321-28.

Lang L, Qi H. The Study of Driver Fatigue Monitor Algorithm Combined PERCLOS and AECS. International Conference on Computer Science and Software Engineering 2008; 1: 349-52.

Li YJ, Fan F. Development of EEG analysis in the research of cognitive science. Beijing Biomedical Engineering 2006; 14(2): 97-100.

Liu L, Liang GZ. Application of EEG analysis in cognitive science. Applied Mechanics & Materials 2014; 519-20, 816-19.

Lin CT, Chen SA, Chiu TT, Lin HZ, Ko LW. Spatial and temporal EEG dynamics of dual-task driving performance. Journal of Neuroengineering & Rehabilitation 2011; 8(1): 11-11.

Lin CT, Chung IF, Ko LW, Chen YC, Liang SF, Duann JR. Eeg-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment. IEEE Transactions on Biomedical Engineering 2007; 54(7): 1349-52.

Lin CT, Lin FC, Chen SA, Lu SW, Chen TC, Ko LW. Eeg-based brain-computer interface for smart living environmental auto-adjustment. Journal of Medical & Biological Engineering 2010; 30(4): 237-45.

Mu Z, Hu J, Yin J. Driving fatigue detecting based on eeg signals of forehead area. International Journal of Pattern Recognition & Artificial Intelligence 2016; 31(05): 40-44.

Murata Y, Yoshida K. Automobile driving interface using gesture operations for disabled people. International Journal on Advances in Intelligent Systems 2013; (3-4): 329-41.

Otmani S, Pebayle T, Roge J, Muzet A. Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers. Physiology & Behavior 2005; 84(5): 715- 24.

Patel M, Lal SKL, Rossiter P, Rossiter P. Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Systems with Applications an International Journal 2011; 38(6): 7235-42.

Philip P, Taillard J, Klein E, Sagaspe P, Charles A, Davies WL. Effect of fatigue on performance measured by a driving simulator in automobile drivers. Journal of Psychosomatic Research 2003; 55(3): 197-200.

Wang Y, Liu X, Zhang Y, Zhu Z, Liu D, Sun J. Driving Fatigue Detection Based on EEG Signal. Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control 2016; 715-18.

Yang G, Lin Y, Bhattacharya P. A driver fatigue recognition model based on information fusion and dynamic bayesian network. Information Sciences 2010; 180(10): 1942-54.

Yildiz A, Akin M, Poyraz M, Kirbas G. Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Systems with Applications an International Journal 2009; 36(4): 7390-99.

Zhao C, Min Z, Liu J, Zheng C. Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator. Accident Analysis & Prevention 2012; 45(1): 83-90.


Supporting Agencies





| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2018