Objective. Electroencephalogram (EEG) is an objective reflection of the brain activities, which provides potential possibilities for brain state estimation based on EEG characteristics. However, how to mine the effective EEG characteristics is still a distressing problem in brain state monitoring. Approach. The phase-scrambled method was used to generate images with different noise levels. Images were encoded into a rapid serial visual presentation paradigm. N-back working memory method was employed to induce and assess fatigue state. The irregular-resampling auto-spectral analysis method was adopted to extract and parameterize (exponent and offset) the characteristics of EEG fractal components, which were analyzed in the four dimensions: fatigue, sustained attention, visual noise and experimental tasks. Main results. The degree of fatigue and visual noise level had positive effects on exponent and offset in the prefrontal lobe, and the ability of sustained attention negatively affected exponent and offset. Compared with visual stimuli task, rest task induced even larger values of exponent and offset and statistically significant in the most cerebral cortex. In addition, the steady-state visual evoked potential amplitudes were negatively and positively affected by the degree of fatigue and noise levels, respectively. Significance. The conclusions of this study provide insights into the relationship between brain states and EEG characteristics. In addition, this study has the potential to provide objective methods for brain states monitoring and EEG modeling.

目标。脑电图（EEG）是大脑活动的客观反映，为基于脑电特征的大脑状态估计提供了潜在的可能性。然而，如何挖掘有效的脑电特征仍然是脑状态监测中令人苦恼的问题。方法. 相位加扰方法用于生成具有不同噪声水平的图像。图像被编码成快速串行视觉呈现范式。采用N-back工作记忆法诱导和评估疲劳状态。采用不规则重采样自动谱分析方法提取和参数化（指数和偏移）脑电分形成分的特征，从疲劳、持续注意力、视觉噪声和实验任务四个维度进行分析。主要结果. 疲劳程度和视觉噪声水平对前额叶的指数和偏移有积极影响，而持续注意的能力对指数和偏移有负面影响。与视觉刺激任务相比，休息任务在大多数大脑皮层中引起更大的指数和偏移值，并且具有统计学意义。此外，稳态视觉诱发电位振幅分别受到疲劳程度和噪音水平的负面和正面影响。意义。这项研究的结论提供了对大脑状态和 EEG 特征之间关系的见解。此外，这项研究有可能为大脑状态监测和脑电图建模提供客观的方法。