A noninvasive real-time driving fatigue detection technology based on left prefrontal Attention and Meditation EEG

Driving fatigue has been considered as a significant risk factor in transportation accidents, and the development of the human cognitive state based on electroencephalogram (EEG) has become a major focus in the field of driving safety. However, it faces portable and real-time problems on its practical application. This study uses MindWave to collect the Attention and Meditation EEG from the left prefrontal lobe of the subject, and uses the relation between Attention and Meditation EEG when the subject is in the state of concentration, relaxation, fatigue and sleep being measured first. As a result, a new method for driving fatigue detection based on the correlation coefficient between drivers Attention and Meditation EEG is proposed. Meanwhile, the k-Nearest Neighbors (k-NN) algorithm is introduced to classify the correlation coefficient between the drivers Attention and Meditation EEG, so as to detect driving fatigue and alert. Lastly, the software running on an Android smart device is developed based on the above technologies, and the experiment proves that it has noninvasive and real-time advantages, while its sensitivity and specificity are 68.31% and 90.43% respectively.

[1]  Evangelos Bekiaris,et al.  Using EEG spectral components to assess algorithms for detecting fatigue , 2009, Expert Syst. Appl..

[2]  T. Sejnowski,et al.  Estimating alertness from the EEG power spectrum , 1997, IEEE Transactions on Biomedical Engineering.

[3]  T. Jung,et al.  Combined eye activity measures accurately estimate changes in sustained visual task performance , 2000, Biological Psychology.

[4]  Guideline Thirteen: Guidelines for Standard Electrode Position Nomenclature , 1994, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[5]  M. Kangas,et al.  Sensitivity and specificity of fall detection in people aged 40 years and over. , 2009, Gait & Posture.

[6]  Mu Li,et al.  Detecting Drowsiness in Driving Simulation Based on EEG , 2008, SJTU-TUB Joint Workshop.

[7]  Koji Oguri,et al.  Estimation of drowsiness level based on eyelid closure and heart rate variability , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Tzyy-Ping Jung,et al.  Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[9]  L F Dell'Osso,et al.  Saccadic velocity characteristics: intrinsic variability and fatigue. , 1979, Aviation, space, and environmental medicine.

[10]  Kyoko Idogawa.  ON THE BRAIN WAVE ACTIVITY OF PROFESSIONAL DRIVERS DURING MONOTONOUS WORK , 1991 .

[11]  Gamini Dissanayake,et al.  Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm , 2011, IEEE Transactions on Biomedical Engineering.

[12]  A. Craig,et al.  Electroencephalography Activity Associated with Driver Fatigue: Implications for a Fatigue Countermeasure Device , 2001 .

[13]  Turgay Tugay Bilgin,et al.  A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data , 2012, IET Commun..

[14]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.