使用微状态强度分析自发脑电图：追踪从警觉到疲劳状态的变化。
Using microstate intensity for the analysis of spontaneous EEG: tracking changes from alert to the fatigue state.
作者信息
Thuraisingham Ranjit A, Tran Yvonne, Craig Ashley, Wijesuriya Nirupama, Nguyen Hung
机构信息
Rehabilitation Studies Unit, University of Sydney.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4982-5. doi: 10.1109/IEMBS.2009.5334094.
Fatigue is a negative symptom of many illnesses and also has major implications for road safety. This paper presents results using a method called microstate segmentation (MSS). It was used to distinguish changes from an alert to a fatigue state. The results show a significant increase in MSS instantaneous amplitude during the fatigue state. Plotting the linear gradient of the nonlinear part of the phase data from the MSS also showed a significant difference (P<0.01) in the gradients of the alert state compared to the fatigue state. The results suggest that MSS can be used in analyzing spontaneous electroencephalography (EEG) signals to detect changes in physiological states. The results have implications for countermeasures used in detecting fatigue.
疲劳是许多疾病的负面症状，对道路安全也有重大影响。本文介绍了使用一种称为微状态分割（MSS）的方法所得到的结果。该方法用于区分从警觉状态到疲劳状态的变化。结果显示，在疲劳状态下，MSS瞬时振幅显著增加。绘制来自MSS的相位数据非线性部分的线性梯度图也显示，与疲劳状态相比，警觉状态的梯度存在显著差异（P<0.01）。结果表明，MSS可用于分析自发脑电图（EEG）信号，以检测生理状态的变化。这些结果对检测疲劳所采用的对策具有启示意义。
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