运动相关脑大电位的时空分布增强

G.C. Filligoi , L. Fattorini
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引用次数: 1

摘要

由于电位分布的空间模糊和对电参考的依赖,传统的脑图存在严重的局限性。表面拉普拉斯函数(SL)已被用于消除与运动相关的脑大电位(MRBM)的模糊,因为它可以作为高通空间滤波器,减少头部体积导体效应。此外,通常用来提高信噪比(SNR)的方法是众所周知的同步平均。然而,当研究对象是扫描变异性时,这种方法不再有效。在这种情况下,原始和拉普拉斯变换的单扫描MRBM可以通过外生输入(ARX)滤波的自回归来提高信噪比。在我们的研究中,单独或联合应用ARX和SL来增强与人类单侧自主自定节奏手指运动相关的单扫MRBM的空间分布。研究表明,当首次使用ARX然后使用SL时,单次扫描大脑映射与生理结果更加一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial and Temporal Distribution Enhancement of Movement-Related Brain Macropotentials

Conventional brain maps suffer from severe limitations due to both the spatial blur of potential distributions and the dependence on electrical reference. The surface Laplacian (SL) has been used to deblur movement-related brain macropotentials (MRBM) since it acts as a high-pass spatial filter that reduces the head volume conductor effects. Moreover, the method usually employed to improve the signal-to-noise ratio (SNR) is the well-known synchronized average. However, this method is no longer valid when the object of the study is the sweep-by-sweep variability. In this case, the SNR of original and Laplacian-transformed single-sweep MRBM can be improved by autoregressive with exogenous input (ARX) filtering. In our study, isolated or combined ARX and SL are applied to enhance the spatial distributions of single-sweep MRBM associated with unilateral voluntary self-paced finger movements in humans. It shows that single-sweep brain mappings are more coherent to physiological findings when ARX is first used followed by SL.

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