基于光学脑电图图像的运动肌肉控制机制研究

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS
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引用次数: 0

摘要

对运动肌肉控制机制的研究可以改善康复治疗和人机交互技术。传统脑电图(EEG)的局限性限制了对运动肌肉控制机制的全面了解。因此,本研究旨在探索基于光学脑电图图像的运动肌肉控制机制,以拓展对运动控制过程的理解。本研究选择光学脑电图成像技术作为主要的数据采集工具。光学脑电图图像具有更高的时空分辨率,能提供更详细的神经活动信息。该技术将光学成像与脑电图图像相结合,可在短时间内获取大脑活动的时空信息。该设备由多个光学传感器组成,可测量大脑皮层的血氧浓度和神经元活动。利用图像处理和信号处理技术对脑电图图像数据进行预处理,然后使用计算方法和算法检测激活区域,并利用相关分析和统计方法评估它们之间的关系。通过比较不同运动任务下的脑电图图像数据和运动肌肉活动数据。研究结果表明,光学脑电图成像技术能提供更详细的脑神经活动信息,并能准确捕捉不同运动肌肉的活动模式。这些成果为进一步研究运动肌肉的控制机制提供了新的视角和方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the mechanism of motor muscle control based on optical EEG images

The study of motor muscle control mechanisms can improve rehabilitation therapy and human-computer interaction technology. The limitations of traditional electroencephalography (EEG) limit the comprehensive understanding of motor muscle control mechanisms. Therefore, this study aims to explore the mechanism of motor muscle control based on optical EEG images, in order to expand the understanding of the process of motor control. The study selected optical EEG imaging technology as the main data acquisition tool. Optical EEG images have higher spatiotemporal resolution and can provide more detailed neural activity information. This technology combines optical imaging with EEG images to obtain spatiotemporal information of brain activity in a short period of time. The device is composed of multiple optical sensors and can measure blood oxygen concentration and neuronal activity in the cerebral cortex. Preprocess EEG image data using image processing and signal processing techniques, then use computational methods and algorithms to detect activated regions, and evaluate their relationships using correlation analysis and statistical methods. By comparing EEG image data and motor muscle activity data under different motor tasks. The research results show that optical EEG imaging technology can provide more detailed information on brain neural activity and accurately capture the activity patterns of different motor muscles. These results provide new perspectives and methods for further studying the control mechanisms of motor muscles.

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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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