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Cognitive event-related potential waveform latency determination: Based on result of matching pursuit algorithm and Hilbert-Huang transform

机译:与认知事件有关的潜在波形潜伏期确定:基于匹配追踪算法和希尔伯特-黄变换的结果

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According to the statistics of car accident causes [11] given by Police of the Czech Republic, about 17.5 % of all car accidents are caused by lack of dedication to driving, including microsleep. A lot of car factories develop systems for fatigue prediction and microsleep detection. These systems are usually based on eye movement tracking or steering wheel movement analysis. But both these methods detect consequences of fatigue. From road safety point of view, it would be useful to be able to detect fatigue itself before it affects dedication to driving. We know there could be a correlation between cognitive event-related potential (ERP) waveform latency and the rate of attention — the longest the latency is, the more tired the measured subject is. This paper deals with determination of latency of a cognitive ERP waveform from outputs of two algorithms we use for its detection and which we have the best experience with.
机译:根据捷克共和国警察提供的车祸原因统计[11],所有车祸中约有17.5%是由于缺乏奉献精神导致的,包括小睡。许多汽车制造厂开发了用于疲劳预测和微睡眠检测的系统。这些系统通常基于眼睛运动跟踪或方向盘运动分析。但是这两种方法都可以检测疲劳的后果。从道路安全的角度来看,能够在疲劳影响敬业度之前检测出疲劳本身将很有用。我们知道,认知事件相关电位(ERP)波形潜伏期与注意力率之间可能存在相关性-潜伏期越长,被测对象越疲劳。本文涉及从我们用于检测的两种算法的输出中确定认知ERP波形的潜伏期,我们对此有最佳的经验。

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