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语音伪影也存在于尖峰数据中。

Speech artifact is also present in spike data.

机构信息

Department of Neurosurgery, University of Iowa, Iowa City, 52242 USA.

Human Neurophysiology & Neuromodulation Lab, Department of Communication Sciences &, Disorders. Louisiana State University, Baton Rouge, 70803 USA.

出版信息

Neuroimage. 2022 Nov;263:119642. doi: 10.1016/j.neuroimage.2022.119642. Epub 2022 Sep 21.

DOI:10.1016/j.neuroimage.2022.119642
PMID:36150607
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11321429/
Abstract

Bush et al. (2022) highlight that brain recordings examining speech production can be significantly affected by microphonic artifact, which would change the interpretation of these kinds of data. While these findings are vital in determining whether data are artifactual or physiological in origin, frequencies were only examined up to 250 Hz (i.e., local field potentials), which would imply that spike-related data (single or multi-neuron recordings) are unaffected. We highlight here that this type of contamination may also be present in unit recordings, with the same aim to understand genuine neural mechanisms rather than mis-interpreting artifactual data.

摘要

布什等人(2022 年)强调,检查言语产生的脑记录可能会受到微音伪迹的显著影响,这将改变对这些数据的解释。虽然这些发现对于确定数据是人为的还是生理的来源至关重要,但频率仅检查到 250 Hz(即局部场电位),这意味着与尖峰相关的数据(单个或多神经元记录)不受影响。我们在这里强调,这种类型的污染也可能存在于单位记录中,目的是了解真正的神经机制,而不是错误地解释人为数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15c7/11321429/393b56a1c0a6/nihms-2011108-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15c7/11321429/1a1e86b260ca/nihms-2011108-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15c7/11321429/393b56a1c0a6/nihms-2011108-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15c7/11321429/1a1e86b260ca/nihms-2011108-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15c7/11321429/393b56a1c0a6/nihms-2011108-f0002.jpg

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本文引用的文献

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2
Machine learning algorithm for decoding multiple subthalamic spike trains for speech brain-machine interfaces.用于解码语音脑机接口中多个丘脑下刺脉冲序列的机器学习算法。
J Neural Eng. 2021 Nov 25;18(6). doi: 10.1088/1741-2552/ac3315.
3
Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography.
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Front Neurosci. 2021 Oct 6;15:725384. doi: 10.3389/fnins.2021.725384. eCollection 2021.
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Observation and assessment of acoustic contamination of electrophysiological brain signals during speech production and sound perception.观察和评估言语产生和声音感知期间电生理脑信号的声学干扰。
J Neural Eng. 2020 Oct 15;17(5):056028. doi: 10.1088/1741-2552/abb25e.
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