College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, Guangdong 518118, China.
College of Heath Science and Environment Engineering, Shenzhen Technology University, Shenzhen, Guangdong 518118, China.
Neural Plast. 2021 Jan 20;2021:3965385. doi: 10.1155/2021/3965385. eCollection 2021.
Mental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the clarification of the mental fatigue mechanism. However, the common method of connectivity analysis based on EEG cannot get rid of the interference from strong noise. In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed. The signal to noise ratio of the extracted feature has been analyzed. Compared with principal component analysis, the proposed method can significantly improve the signal to noise ratio and suppress the noise interference. The proposed method has been applied on the analysis of mental fatigue connectivity. The causal connectivity among the frontal, motor, parietal, and visual areas under the awake, fatigue, and sleep deprivation conditions has been analyzed, and different patterns of connectivity between conditions have been revealed. The connectivity direction under awake condition and sleep deprivation condition is opposite. Moreover, there is a complex and bidirectional connectivity relationship, from the anterior areas to the posterior areas and from the posterior areas to the anterior areas, under fatigue condition. These results imply that there are different brain patterns on the three conditions. This study provides an effective method for EEG analysis. It may be favorable to disclose the underlying mechanism of mental fatigue by connectivity analysis.
精神疲劳是由长时间认知活动引起的常见心理生物学状态。虽然精神疲劳的表现和缺点已经广为人知，但它在大脑多个区域之间的连通性尚未得到彻底研究。这对于澄清精神疲劳机制很重要。然而，基于 EEG 的连通性分析的常用方法无法摆脱强噪声的干扰。在本文中，提出了一种基于堆叠去噪自动编码器的自适应特征提取模型。对提取特征的信噪比进行了分析。与主成分分析相比，该方法可以显著提高信噪比并抑制噪声干扰。该方法已应用于精神疲劳连通性的分析。在清醒、疲劳和睡眠剥夺条件下，分析了额区、运动区、顶区和视觉区之间的因果连通性，揭示了不同条件下的连通性模式。清醒状态和睡眠剥夺状态下的连通方向相反。此外，疲劳状态下从前区到后区和从后区到前区存在复杂的双向连通关系。这些结果表明，在这三种情况下大脑模式不同。本研究为 EEG 分析提供了一种有效的方法。通过连通性分析揭示精神疲劳的潜在机制可能是有利的。
