CAJ | 학술논문

为了实时识别异常驾驶状态,实现驾驶疲劳的提前预报,在驾驶模拟舱中采用脑电仪采集驾驶员脑电数据.采用功率谱估计分析正常阶段和疲劳过渡阶段的脑电数据,计算不同频段波能量分布情况,结果表明delta和alpha波在两种状态下变化显著,因此跟踪监测delta和alpha波能量变化能够实现驾驶疲劳的早期预报.为此,利用径向基神经网络构建预报系统,检验delta波的预报效果,同时提取delta波能量谱的全部极大值并通过多义线拟合,有效地提高预报性能.
위료실시식별이상가사상태,실현가사피로적제전예보,재가사모의창중채용뇌전의채집가사원뇌전수거.채용공솔보고계분석정상계단화피로과도계단적뇌전수거,계산불동빈단파능량분포정황,결과표명delta화alpha파재량충상태하변화현저,인차근종감측delta화alpha파능량변화능구실현가사피로적조기예보.위차,이용경향기신경망락구건예보계통,검험delta파적예보효과,동시제취delta파능량보적전부겁대치병통과다의선의합,유효지제고예보성능.
In order to recognize abnormal driving state and realize driving fatigue early warning, the EEG data for a driver was recorded by an EEG apparatus in the Auto Sim, and analyzed between the awake and fatigue transitional phases by power spectrum estimation, the power distribution with frequency bands between two phases was given. The result shows delta and alpha activities are possible to predict driver fatigue early. So the prediction system was created by radical basis network to test the prediction effect of the delta activity and extracted all the maximums and used spline fitting in order to improve prediction performance.