Author(s): Zhang, Shangen; Yan, Xinyi; Wang, Yijun; Liu, Baolin; Gao, Xiaorong

Source:JOURNAL OF NEURAL ENGINEERING Volume:18 Issue:5 Article Number:56047 DOI:10.1088/1741-2552/ac2628 Published:OCT 2021

Abstract: 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.

Accession Number:WOS:000703691900001

ISSN: 1741-2560

eISSN: 1741-2552

Full Text: https://iopscience.iop.org/article/10.1088/1741-2552/ac2628