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EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

机译:基于2型模糊分类器的驾驶认知故障检测的脑电分析。

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This paper aims at detecting online cognitive failures in driving by decoding the electroencephalography (EEG) signals acquired during visual alertness, motor planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the preprocessed EEG signals obtained from his prefrontal and frontal lobes into two classes: alert and nonalert. Motor planning performed by the driver using the preprocessed parietal signals is classified into four classes: braking, acceleration, steering control, and no operation. Cognitive failures in motor planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the copilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time delay between the onset of motor imagination and the electromyogram response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration, and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/h, the lead time is more than 600 ms, which offer a safe distance of 10.66 m.
机译:本文旨在通过解码驾驶员的视觉警报,运动计划和运动执行阶段中获取的脑电图(EEG)信号,来检测驾驶中的在线认知失败。通过将从其前额叶和额叶获得的预处理EEG信号分为两类,可以检测驾驶员的视觉机敏性:警报和非警报。驾驶员使用预处理的顶信号执行的电机计划分为四类:制动,加速,转向控制和无操作。通过将驾驶员的分类运动计划等级与通过手持式旋转开关从副驾驶员处获得的地面真实等级进行比较,可以确定运动计划中的认知故障。最后,当运动想象力开始和肌电图反应之间的时间延迟超过预定义的持续时间时,检测到运动执行失败。本研究最重要的方面在于计划阶段的认知失败分类。主观计划分类的复杂性是由于制动,加速和转向控制中涉及的信号特征可能重叠造成的。采用专门的区间/一般2型模糊集诱导神经分类器来消除运动计划分类的不确定性。进行的实验表明,在存在驾驶员外部干扰的情况下,提出的神经模糊分类器优于传统技术。视觉警报和运动执行的解码是使用内核化支持向量机分类器执行的。分析表明,在以64 km / h的速度行驶时,前置时间超过600毫秒,安全距离为10.66 m。

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