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Updated: Feb 11, 2026

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使用TMS导航系统对传感器位置进行高精度数字化,以改进源定位

Marina Morozova¹, Lev Yakovlev², Nikolay Syrov³

¹Center for Bio- and Medical Technologies, Skolkovo Institute of Science and Technology; Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education; m.morozova.v@yandex.ru.

Journal of visualized experiments : JoVE

|February 9, 2026

概括

本研究介绍了一种使用导航脑刺激系统 (NBS) 数字化电脑图 (EEG) 传感器位置的实用方法. 这种方法提高了大脑源部位定位的准确性,而不需要新的设备.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
信号处理信号处理

背景情况:

在神经科学中,准确的源定位依赖于传感器位置的精确空间数字化.
现有的跨磁刺激 (TMS) 基础设施,特别是导航脑刺激 (NBS) 系统,在脑电图 (EEG) 和功能近红外光谱 (fNIRS) 传感器数字化方面未得到充分利用.
专门的数字化设备可能很昂贵,对许多研究实验室构成障碍.

研究的目的:

通过使用Nexstim NBS系统来呈现一种实用,可靠和具有成本效益的EEG传感器位置数字化方法.
展示如何将数字化传感器数据集成到EEG预处理和源定位管道中.
在EEG和fNIRS研究中提高源建模的空间精度和解剖相关性.

主要方法:

使用Nexstim NBS系统的默认工具来数字化EEG电极位置.
导出坐标数据并使用自定义 Python 脚本进行处理.
将数字化数据集成到MNE-Python中,以进行源本地化,包括与MRI图像对齐和共同注册优化.

主要成果:

该协议提供了一个完整的工作流,从数字化到源估计.
定制的Python脚本有助于协调处理和联合注册,确保可重现性.
与标准电极组装相比,结合数字化电极位置显著提高了皮质源估计的解剖学准确性和可解释性.

结论:

导航脑刺激 (NBS) 系统为准确的EEG传感器数字化提供了一个实用的解决方案.
利用现有的TMS基础设施减少了对专门数字化设备的需求.
这种方法提高了脑源定位技术的可靠性和解剖学有效性.

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