基于脑电信号分析的不同年龄驾驶人疲劳特性

为研究不同年龄驾驶人驾驶过程中疲劳情况及疲劳累积速度，对比其疲劳产生与变化的差异性，获取不同年龄驾驶人的最优驾驶时间，设计自然驾驶试验，利用Physio生理多导仪采集脑电数据，并采用主观检测方法对驾驶人进行问询。应用MATLAB对采集到的脑电数据进行降噪处理，通过积分获取各时段α波、β波和θ波的平均功率谱密度，进而求得脑电指标R_（α/β），R_（θ/β），R_{（α+θ）/β}。利用SPSS将其与驾驶时间进行单因素方差分析，并通过敏感性判断，选取R_{（α+θ）/β}作为驾驶疲劳表征指标。对各年龄段驾驶人的R_{（α+θ）/β}进行均值化处理，并将其与驾驶时间进行线性拟合，分析驾驶人年龄对驾驶疲劳累积速度的影响。对驾驶过程中各时段的R_{（α+θ）/β}进行配对样本t检验，并结合主观问询结果确定不同年龄驾驶人的最优驾驶时间。研究结果表明：青年和中年驾驶人在0~1.5 h内疲劳累积速度相对缓慢，老年驾驶人较快；在1.5~3 h内，青年驾驶人疲劳累积速度最快，中年驾驶人最慢；老、中、青年驾驶人的最优驾驶时间分别为60~75，120~135，105~120 min；不同年龄驾驶人其驾驶经验、体力和精力及外界环境干扰是影响疲劳累积速度的重要因素；试验结果验证了采用R_{（α+θ）/β}作为驾驶疲劳表征指标的有效性，有助于为不同年龄驾驶人安全驾驶时长的确定提供科学依据。

Fatigue Characteristics in Drivers of Different Ages Based on Analysis of EEG

To determine the status of fatigue and the speed of fatigue accumulation in drivers of different ages during driving, we compared the difference in fatigue generation and change to obtain the optimal driving time for drivers of different ages, and a natural driving experiment was designed in our study. The electroencephalogram (EEG) data of drivers was collected through Physio, a physiological multichannel instrument. The drivers were inquired by the subjective test method simultaneously. MATLAB was applied to denoise the collected EEG data and the average densities of power spectrum of the α wave, β wave, and θ wave in each period were calculated through integration. Subsequently, the EEG indexes, R_(α/β), R_(θ/β), and R_(α+θ)/β, were obtained. The one-way analysis of variance (ANOVA) was performed by comparing them with the driving time using SPSS. R_(α+θ)/β was chosen as the characterization index of driving fatigue through sensitivity judgment. The R_(α+θ)/β values in drivers of different ages were averaged and were fitted linearly with the driving time such that the impact of the driver's age on the speed of driving fatigue accumulation can be analyzed. The t tests of paired samples were performed on R_(α+θ)/β in each period during driving combined with the subjective inquiry results; the optimal driving time in drivers of different ages was obtained. The result shows that the speed of fatigue accumulation of young and middle-aged drivers within 0-1.5 h is relatively slow, while that of the elderly drivers is fast. However, that of the young drivers is the fastest within 1.5-3 h, while that of the middle-aged drivers is the slowest. The optimal driving time of the elderly, middle-aged and young drivers is 60-75 min, 120-135 min, and 105-120 min. Driving experience, physical strength, the energy in drivers of different ages, and the disturbance of external environment are the important factors that affect the speed of fatigue accumulation. The experiment results verify the effectiveness of using R_(α+θ)/β as the driving fatigue characterization index, which will help to afford scientific evidence to set a safe driving time for drivers of different ages.