Frequency, time, and spatial electroencephalogram changes after COVID-19 during a simple speech task

Darya Vorontsova, M. V. Isaeva, I. Menshikov, Kirill Orlov, Alexandra Bernadotte
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Abstract

We found a predominance of α -rhythm patterns in the left hemisphere in healthy people compared to people with COVID-19 history. Moreover, we observe a significant decrease in the left hemisphere contribution to the speech center area in people who have undergone COVID-19 when performing speech tasks. Our findings show that the signal in healthy subjects is more spatially localized and synchronized between hemispheres when performing tasks compared to people who recovered from COVID-19. We also observed a decrease in low frequencies in both hemispheres after COVID-19. EEG-patterns of COVID-19 are detectable in an unusual frequency domain. What is usually considered noise in electroencephalographic (EEG) data carries information that can be used to determine whether or not a person has had COVID-19. These patterns can be interpreted as signs of hemispheric desynchronization, premature brain ageing, and more significant brain strain when performing simple tasks compared to people who did not have COVID-19. In our work, we have shown the applicability of neural networks in helping to detect the long-term effects of COVID-19 on EEG-data. Furthermore, our data following other studies supported the hypothesis of the severity of the long-term effects of COVID-19 detected on the EEG-data of EEG-based BCI. The presented findings of functional activity of the brain– computer interface make it possible to use machine learning methods on simple, non-invasive brain–computer interfaces to detect post-COVID syndrome and develop progress in neurorehabilitation.
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在简单的言语任务中,COVID-19后脑电图的频率、时间和空间变化
我们发现,与有COVID-19病史的人相比,健康人的左半球α -节律模式占优势。此外,我们观察到患有COVID-19的人在执行语音任务时左半球对语言中心区域的贡献显着减少。我们的研究结果表明,与从COVID-19中恢复的人相比,健康受试者在执行任务时的信号在空间上更局部,在半球之间更同步。我们还观察到,在COVID-19之后,两个半球的低频也有所减少。可在异常频域检测到COVID-19的脑电图模式。脑电图(EEG)数据中通常被认为是噪声的信息可用于确定一个人是否患有COVID-19。与未感染COVID-19的人相比,这些模式可以解释为大脑半球失同步、大脑过早老化以及在执行简单任务时更严重的大脑紧张的迹象。在我们的工作中,我们已经证明了神经网络在帮助检测COVID-19对脑电图数据的长期影响方面的适用性。此外,在其他研究的基础上,我们的数据支持了基于脑电图的脑机接口的脑电图数据中检测到的COVID-19长期影响严重程度的假设。脑机接口功能活动的研究结果使得在简单、无创的脑机接口上使用机器学习方法来检测covid - 19后综合征并在神经康复方面取得进展成为可能。
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来源期刊
Computer Research and Modeling
Computer Research and Modeling Computer Science-Computational Theory and Mathematics
CiteScore
0.80
自引率
0.00%
发文量
82
审稿时长
15 weeks
期刊介绍: The journal publishes original research papers and review articles in the field of computer research and mathematical modeling in physics, engineering, biology, ecology, economics, psychology etc. The journal covers research on computer methods and simulation of systems of various nature in the leading scientific schools of Russia and other countries. Of particular interest are papers devoted to simulation in thriving fields of science such as nanotechnology, bioinformatics, and econophysics. The main goal of the journal is to cover the development of computer and mathematical methods for the study of processes in complex structured and developing systems. The primary criterion for publication of papers in the journal is their scientific level. The journal does not charge a publication fee. The decision made on publication is based on the results of an independent review. The journal is oriented towards a wide readership – specialists in mathematical modeling in various areas of science and engineering. The scope of the journal includes: — mathematical modeling and numerical simulation; — numerical methods and the basics of their application; — models in physics and technology; — analysis and modeling of complex living systems; — models of economic and social systems. New sections and headings may be included in the next volumes.
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