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

Abstract

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.

ARTIST: A fully automated artifact rejection algorithm for single‐pulse TMS‐EEG data

“…The workflow of ARTIST is similar to the one described in TESA (Rogasch et al, 2016) and TMSEEG (Atluri et al, ), but unlike those other packages, here the IC rejection is fully automated. In particular, a semi‐automated algorithm for IC selection was provided in TESA, in which the ICs were classified based on heuristically defined thresholds, as opposed to the thresholds determined in a data‐driven manner as in ARTIST, and the IC classification accuracy was not reported.…”

“…As spTMS-EEG data are considered noisier than standard EEG data due to stimulation-induced artifacts, various additional methods were developed (Atluri et al, 2016;Casula et al, 2017;Hernandez-Pavon et al, 2012;Herring, Thut, Jensen, & Bergmann, 2015;Korhonen et al, 2011;Mutanen et al, 2016;Rogasch et al, 2014Rogasch et al, , 2016. In general, these methods used signal projection techniques to find spatial filters that could suppress the artifacts while leaving the neural signals largely intact.…”

Short interval intracortical inhibition as measured by TMS-EEG

“…Conversely, TEP analysis has several steps such as filtering, noise reduction and independent component analysis. While, we followed the steps for a previously established TEP analysis pipeline (13,34), each step has a number of assumptions that manipulates the data. While the underlying process may be the same, the difference in the approaches may alter the output and in effect 'hide' the effects of cortical inhibition from TEP analysis.…”

How can transcranial magnetic stimulation be used to causally manipulate memory representations in the human brain?

“…Hoping to improve and standardize analysis across the field of TMS-EEG research, Rogasch et al (2017) released the TMS-EEG signal analyzer (TESA), an open-source extension for EEGLAB that includes functions that are specific for TMS-EEG analysis. Other open source toolboxes include TMSEEG (Atluri et al, 2016) and code within the FieldTrip toolbox (see Herring, Thut, Jensen, & Bergmann, 2015).…”