Harel Asaf, Levkovsky Anna, Nakdimon Idan, Gordon Barak, Shriki Oren
Department of Cognitive and Brain Sciences, Ben Gurion University of the Negev, Beer-Sheva, Israel.
Israeli Air Force Aeromedical Center, Ramat Gan, Israel.
Sleep. 2025 Jul 11;48(7). doi: 10.1093/sleep/zsaf080.
Prolonged wakefulness is known to adversely affect basic cognitive abilities such as object recognition and decision-making. It affects the dynamics of neuronal networks in the brain and can even lead to hallucinations and epileptic seizures. In cognitive-intensive workplaces, there is a requirement to refine an objective method of quantifying the current level of cognitive capabilities, rather than relying on subjective self-reporting. In this study, we compiled electroencephalography (EEG) recordings from several sleep-deprivation workshops held by the Israeli Air Force, done by flight cadets and unmanned aerial vehicle operators. By extracting a wide range of EEG features and applying machine-learning techniques, we were able to accurately predict the reaction time of participants undergoing a simple psychomotor vigilance task, which acted as a stand-in for basic cognitive functions. Furthermore, through the use of interpretability methods, we examined the importance of different EEG features and their contribution to changes in the behavioral metrics.
众所周知，长时间清醒会对诸如物体识别和决策等基本认知能力产生不利影响。它会影响大脑神经网络的动态变化，甚至可能导致幻觉和癫痫发作。在认知要求较高的工作场所，需要完善一种客观方法来量化当前的认知能力水平，而不是依赖主观的自我报告。在本研究中，我们收集了以色列空军举办的多个睡眠剥夺研讨会的脑电图（EEG）记录，这些记录由飞行学员和无人机操作员完成。通过提取广泛的EEG特征并应用机器学习技术，我们能够准确预测参与简单心理运动警觉任务的参与者的反应时间，该任务可作为基本认知功能的替代指标。此外，通过使用可解释性方法，我们研究了不同EEG特征的重要性及其对行为指标变化的贡献。
