Power Analysis of EEG Data in Multiple Brain Regions during Fatigue Driving

The motor driver fatigue is a major cause of traffic accidents. In this paper, we designed a highway simulated scene by Unity3D software, and collected EEG data from the brain in the simulation experiment. The autoregressive (AR) model spectrum estimation method gives better frequency resolution and is widely used for electroencephalogram (EEG) analysis. The EEG data in different rhythms were studied by AR burg power spectrum method during different phases of simulated driving. The results demonstrated that the relative average power spectrum of the EEG data was significantly increased in the $\delta$-rhythm of the most brain regions during the post-driving process, and the θ- and α-rhythms were significantly increased of the prefrontal region and the occipital region. At the same time, the EEG indicators R(α/β),R(δ/β), and R((α+β)/δ) were very sensitive to driver fatigue and had significant changes in the post-driving process. With the increase of simulated driving time, R(α/β) and R(α/β) were increase significantly, however, R((α+β)/δ) decreased significantly. This important topic of brain fatigue can provide an objective and effective indicator for studying driving fatigue.

[1]  Antoine Picot,et al.  On-Line Detection of Drowsiness Using Brain and Visual Information , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2]  Tzyy-Ping Jung,et al.  EURASIP Journal on Applied Signal Processing 2005:19, 3165–3174 c ○ 2005 Hindawi Publishing Corporation Estimating Driving Performance Based on EEG Spectrum Analysis , 2005 .

[3]  Chunxiao Han,et al.  Complexity Analysis of EEG Signals for Fatigue Driving Based on Sample Entropy , 2018, 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[4]  A. Craig,et al.  A critical review of the psychophysiology of driver fatigue , 2001, Biological Psychology.

[5]  Min Zhao,et al.  AUTOMATIC CLASSIFICATION OF DRIVING MENTAL FATIGUE WITH EEG BY WAVELET PACKET ENERGY AND KPCA-SVM , 2010 .

[6]  Jiang Wang,et al.  Power spectral density and coherence analysis of Alzheimer’s EEG , 2014, Cognitive Neurodynamics.

[7]  Abdulhamit Subasi,et al.  Analysis of EEG Signals Under Flash Stimulation for Migraine and Epileptic Patients , 2011, Journal of Medical Systems.

[8]  Olga Sourina,et al.  EEG Based Stress Monitoring , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[9]  Olga Sourina,et al.  Real-time EEG-based emotion monitoring using stable features , 2015, The Visual Computer.

[10]  Ann Williamson,et al.  The link between fatigue and safety. , 2011, Accident; analysis and prevention.

[11]  Hongbo Liu,et al.  Automatic Calibration Method for Driver's Head Orientation in Natural Driving Environment , 2013, IEEE Transactions on Intelligent Transportation Systems.

[12]  Muhammad Reza Pourshahabi,et al.  A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection , 2014 .

[13]  Chun-Hsiang Chuang,et al.  Wireless and Wearable EEG System for Evaluating Driver Vigilance , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[14]  Jiang Wang,et al.  Investigation of EEG abnormalities in the early stage of Parkinson’s disease , 2013, Cognitive Neurodynamics.

[15]  Chun-Hsiang Chuang,et al.  An EEG-Based Fatigue Detection and Mitigation System , 2016, Int. J. Neural Syst..

[16]  Yong Hu,et al.  Changes of Spontaneous Oscillatory Activity to Tonic Heat Pain , 2014, PloS one.

[17]  Shigang Cui,et al.  Power spectrum analysis of EEG signals evoked by LED acupuncture in healthy subjects , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[18]  Jian Xu,et al.  A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation , 2016, Sensors.

[19]  Guido Sanguinetti,et al.  Single-trial classification of EEG in a visual object task using ICA and machine learning , 2014, Journal of Neuroscience Methods.

[20]  Lin Hou Chew,et al.  Aesthetic preference recognition of 3D shapes using EEG , 2015, Cognitive Neurodynamics.

[21]  Nida Itrat Abbasi,et al.  A novel real-time driving fatigue detection system based on wireless dry EEG , 2018, Cognitive Neurodynamics.

[22]  F. Schlindwein,et al.  A study on the optimum order of autoregressive models for heart rate variability. , 2002, Physiological measurement.

[23]  R. Acharya,et al.  Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques , 2008 .

[24]  Gang Li,et al.  Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier , 2013, Sensors.

[25]  Siti Anom Ahmad,et al.  Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task , 2015, Sensors.

[26]  James A. Rodger,et al.  Reinforcing inspiration for technology acceptance: Improving memory and software training results through neuro-physiological performance , 2014, Comput. Hum. Behav..

[27]  James A. Rodger.  NeuroIS Knowledge Discovery Approach to Prediction of Traumatic Brain Injury Survival Rates: A Semantic Data Analysis Regression Feasibility Study , 2015 .

[28]  Ying Sun,et al.  Asynchronous P300 BCI: SSVEP-based control state detection , 2010, 2010 18th European Signal Processing Conference.