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在乒乓球运动中使用双层脑电图来表征和去除伪迹。

Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis.

作者信息

Studnicki Amanda, Downey Ryan J, Ferris Daniel P

机构信息

J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.

出版信息

Sensors (Basel). 2022 Aug 5;22(15):5867. doi: 10.3390/s22155867.

DOI:10.3390/s22155867
PMID:35957423
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9371038/
Abstract

Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research.

摘要

研究人员可以通过研究在现实世界环境中运动的人类来提高大脑研究的生态效度。最近的研究表明,双层脑电图可以提高步态期间皮层电记录的保真度,但尚不清楚这些积极结果是否能外推到非运动范式。在我们的研究中,当参与者打乒乓球时,我们用双层脑电图记录大脑活动,乒乓球是一项全身响应性运动,有助于研究视觉运动反馈、物体拦截和表现监测。我们用时频分析来表征伪迹,并将头皮和参考噪声数据进行关联,以确定不同传感器捕捉伪迹的能力。正如预期的那样,单个头皮通道与噪声匹配通道时间序列的相关性高于与头部和身体加速度的相关性。然后,我们比较了使用和不使用双层噪声电极时的伪迹去除方法。独立成分分析将通道分离成成分,我们根据偶极子模型拟合情况并使用自动标记算法来计算高质量大脑成分的数量。我们发现,使用噪声电极进行数据处理能得到更纯净的大脑成分。这些结果推进了在需要全身运动的人类行为中记录高保真大脑动态的技术方法,这将对脑科学研究有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbe1/9371038/53c87ba39b51/sensors-22-05867-g008.jpg
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