Atluri Sravya, Frehlich Matthew, Mei Ye, Garcia Dominguez Luis, Rogasch Nigel C, Wong Willy, Daskalakis Zafiris J, Farzan Faranak
Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental HealthToronto, ON, Canada; Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada.
Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental HealthToronto, ON, Canada; Department of Electrical and Computer Engineering, University of TorontoToronto, ON, Canada.
Front Neural Circuits. 2016 Oct 7;10:78. doi: 10.3389/fncir.2016.00078. eCollection 2016.
Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research.
在经颅磁刺激(TMS)过程中同步记录脑电图(EEG)是一种用于研究大脑健康和功能的新兴且强大的工具。尽管跨神经科学学科对TMS-EEG适配的兴趣日益浓厚,但其广泛应用受到信号处理挑战的限制。这些挑战源于TMS的特性以及EEG对伪迹的敏感性,而伪迹常常掩盖TMS诱发电位(TEP)。随着数据处理方法复杂性的增加以及对多站点数据整合兴趣的增长,TMS-EEG数据的分析需要开发一种标准化方法,以从各种伪迹源中恢复TEP。本文介绍了TMSEEG,这是一个基于MATLAB的开源应用程序,由多种算法组成,这些算法被组织起来以促进TMS-EEG信号处理的逐步流程。该工具箱采用模块化设计和交互式图形用户界面(GUI),旨在为新手和有经验的用户简化TMS-EEG信号处理。具体而言,TMSEEG提供:(i)针对性去除TMS诱发的和一般的EEG伪迹;(ii)具有灵活性的逐步模块化工作流程,可修改现有算法并添加定制算法;(iii)对伪迹的全面显示和量化;(iv)在整个数据处理工作流程中带有TEP视觉反馈的质量控制检查点;以及(v)标记和存储伪迹数据库的能力。除了这些功能外,TMSEEG的软件架构确保用户在每个处理步骤的初始设置和参数配置中付出的努力最小。这部分是通过与EEGLAB紧密集成来实现的,EEGLAB是一个广泛使用的用于EEG信号处理的开源工具箱。在本文中,我们介绍了TMSEEG,验证了其功能,并展示了其在跨多个单脉冲和多脉冲TMS协议提取TEP中的应用。作为首个基于GUI的用于TMS-EEG信号处理的开源管道,该工具箱旨在促进大脑研究中一种新兴技术的广泛应用和标准化。