TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation

• Published in: Frontiers in neural circuits Vol. 10; p. 78
• Main Authors: Atluri, Sravya, Frehlich, Matthew, Mei, Ye, Garcia Dominguez, Luis, Rogasch, Nigel C, Wong, Willy, Daskalakis, Zafiris J, Farzan, Faranak
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 07-10-2016; Frontiers Media S.A
• Subjects: Algorithms; Brain Mapping; Brain Mapping - methods; Data processing; EEG; Electroencephalography; Electroencephalography - methods; Humans; Independent Component Analysis; Integration; Magnetic fields; MATLAB; Medical Informatics Applications; Nervous system; Neuroscience; Quality control; Signal processing; Signal Processing, Computer-Assisted; Standardization; Transcranial Magnetic Stimulation; Transcranial Magnetic Stimulation - methods; User interface; Visual perception; Canada; brain mapping; electroencephalography; independent component analysis; MATLAB toolbox; signal processing; artifact correction; transcranial magnetic stimulation; standardized workflow
• ISSN: 1662-5110; 1662-5110
• DOI: 10.3389/fncir.2016.00078

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.