驾驶员脑电特征与手臂操纵驾驶行为研究
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摘要
为了进一步研究脑电信号与驾驶行为的关系,通过脑电采集设备与惯性运动捕捉系统同时捕捉驾驶员脑电信号数据与操作手臂运动参数。分析驾驶员手臂驾驶方向盘的过程中,脑电信号的变化与操作手臂运动方向之间的关系。利用共同空间模式(CSP)与小波包分解(WPD)的方式,提取左右运动相关的C3和C4导联特征,结合手臂运动关节的加速度进行相关性分析。实验结果表明:在实际的驾驶行为中,左右转向过程,C3、C4导联出现持续的ERD/ERS现象,为脑机接口控制量化标准提供基础。
In order to further study the relationship between EEG signals and driving behavior,the EEG signal data and the arm motion parameters of the driver are collected simultaneously using the Neuroscan cap and Inertial Measurement System. The relationship between the EEG signals and the movement direction of the driving arms is analyzed in the process that the driver is holding the steering wheel to conduct driving operation. The CSP and WPD are used to extract the features of the electrodes C3 and C4 related with the motion acceleration of the driving hands; and the correlation analysis is performed combining the motion acceleration of the arm movement joints. The experiment result demonstrates that in the real driving behavior,continuous ERD / ERS appear in leads C3 and C4 in the left / right turning process,which provides a foundation for the quantitative standard of BCI control.
In order to further study the relationship between EEG signals and driving behavior,the EEG signal data and the arm motion parameters of the driver are collected simultaneously using the Neuroscan cap and Inertial Measurement System. The relationship between the EEG signals and the movement direction of the driving arms is analyzed in the process that the driver is holding the steering wheel to conduct driving operation. The CSP and WPD are used to extract the features of the electrodes C3 and C4 related with the motion acceleration of the driving hands; and the correlation analysis is performed combining the motion acceleration of the arm movement joints. The experiment result demonstrates that in the real driving behavior,continuous ERD / ERS appear in leads C3 and C4 in the left / right turning process,which provides a foundation for the quantitative standard of BCI control.
引文
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[2]王炳浩,魏建勤,吴永红.汽车驾驶员瞌睡状态脑电波特征的初步探索[J].汽车工程.2004,26(1):70-72.WANG B H,WEI J Q,WU Y H.A preliminary investigation into the brain wave characters of car drivers at dozy state[J].Automotive Engineering,2004,26(1):70-72.
[3]王斐,王少楠,王惜慧,等.基于脑电图识别结合操纵特征的驾驶疲劳检测[J].仪器仪表学报,2014,35(2):398-404.WANG F,WANG SH N,WANG X H,et al.Driving fatigue detection based on EEG recognition and vehicle handling characteristics[J].Chinese Journal of Scientific Instrument.2014,35(2):398-404.
[4]ZICH C,DE VOS M,KRANCZIOCH C,et al.Wireless EEG with individualized channel layout enables efficient motor imagery training[J].Clinical Neurophysiology.2015,126(4):698-710.
[5]王福旺,王宏.长途客车驾驶员疲劳状态脑电特征分析[J].仪器仪表学报,2013,34(5):1146-1152.WANG F W,WANG H.EEG characteristic analysis of coach bus drivers in fatigue state[J].Chinese Journal of Scientific Instrument,2013,34(5):1146-1152.
[6]LI Q,YOUNG M,NAING V,et al.Walking speed estimation using a shank-mounted inertial measurement unit[C].Journal of Biomechanics,2010,43(8):1640-1643.
[7]单庆晓,章明沛,陈权伟.基于惯性的人体行走能量收集与供电技术研究[J].电子测量与仪器学报,2009,23(12):70-74.SHAN Q X,ZHANG M P,CHEN Q W.Inertia energy harvest of human walking to power mobile electronic[J].Journal of Electronic Measurement and Instrumentation,2009,23(12):70-74.
[8]孙伟,初婧,丁伟,等.基于IMU旋转的MEMS器件误差调制技术研究[J].电子测量与仪器学报,2015,29(2):240-246.SUN W,CHU J,DING W,et al.Research on error modulation technology of MEMS based on IMU rotation[J].Journal of Electronic Measurement and Instrumentation,2015,29(2):240-246.
[9]汪少初,刘昱,郝文飞,等.基于惯性传感的人员行进动作识别方法[J].电子测量与仪器学报,2014,28(6):630-636.WANG SH CH,LIU Y,HAO W F,et al.Walking pattern recognition based on inertial sensing[J].Journal of Electronic Measurement and Instrumentation,2014,28(6):630-636.
[10]LAL S K L,CRAIG A,BOORD P,et al.Development of an algorithm for an EEG-based driver fatigue countermeasure[J].Journal Safety and Research,2003,34(3):321-328.
[11]徐宝国,宋爱国.基于小波包变换和聚类分析的脑电信号识别方法[J].仪器仪表学报,2009,30(1):25-28.XU B G,SONG AI G.EEG signal recognition method based on wavelet packet transform and clustering analysis[J].Chinese Journal of Scientific Instrument,2 0 0 9,3 0(1):2 5-2 8.
[12]杨帮华,陆文宇,何美燕,等.脑机接口中基于WPD和CSP的特征提取[J].仪器仪表学报,2012,33(11):2560-2565.YANG B H,LU W Y,HE M Y,et al.Novel feature extraction method for BCI based on WPD and CSP[J].Chinese Journal of Scientific Instrument,2012,33(11):2560-2565.
[13]常文文,王宏.基于P300幅值几何差和脑网络特征的测谎方法研究[J].仪器仪表学报,2015,36(4):822-829.CHANG W W,WANG H.Study on the lie detection method based on P300 amplitude geometry difference and brain network characteristic[J].Chinese Journal of Scientific Instrument,2015,36(4):822-829.
[14]段立飞,高振海,王德平.驾驶员对汽车方向的自适应控制行为建模[J].机械工程学报.2011,47(8):122-125.DUAN L F,GAO ZH H,WANG D P.Modeling of driver’s adaptive control behavior for vehicle direction[J].Journal of Mechanical Engineering,2011,47(8):122-125.
[15]张希波,成波,冯睿嘉.基于方向盘操作的驾驶人疲劳状态实时检测方法[J].清华大学报:自然科学版,2010,50(7):1072-1076,1081.ZHANG X B,CHENG B,FENG R J.Real-time detection of driver drowsiness based on steering performance[J].Journal of Tsinghua University:Science&Engineering,2010,50(7):1072-1076,1081.
[16]陈勇,黄琦,刘霞,等.一种全天候驾驶员疲劳检测方法研究[J].仪器仪表学报,2009,30(3):636-640.CHEN Y,HUANG Q,LIU X et al.All weather detection method of driver fatigue[J].Chinese Journal of Scientific Instrument,2009,30(3):636-640.
[17]PATEL M,LAL S K L,KAVANAGH D,et al.Applying neural network analysis on heart rate variability data to assess driver fatigue[J].Expert Systems with Applications,2011,38(6):7235-7242.
[18]CAI H,LIN L Y,CHENG B.Coordinating multi-level cognitive assistance and incorporating dynamic confidence information in driver-vehicle interfaces[J].Human Factors and Ergonomics in Manufacturing&Service Industries,2012,22(5):437-449.
[19]LIN Y,LENG H,MOURANT R R.EEG electrode locations and their signal sensitivities in driver drowsiness measurement[J].Biomedical Soft Computing and Human Sciences,2009,14(2):97-103.