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.

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The neural basis of decision-making is extremely complex due to the large number of factors that contribute to the outcome of even the most basic actions as well as the range of appropriate responses within many behavioral contexts. To better understand the neural processes underlying basic forms of decision-making, this study utilized an experiment that required a choice about whether to press a button with the right or left hand. These instances of decision-making were compared to identical button presses that were experimentally specified rather than selected by the subject. Magnetoencephalography (MEG) was used to record neural activity during these—what are being termed—free and forced actions and differences in the MEG signal between these two conditions were attributed to the distinct forms of neural activity required to carry out the two types of actions. To produce instances of free and forced behavior, cued button-pressing experiments were performed that use visual, aural, and memorized cues to instruct experimental subjects of the expected outcome of individual trials. Classification analysis of the trials revealed that cortical regions that allowed for the most accurate classification of free and forced actions primarily handle sensory input for the modality used to cue the trials: occipital cortex for visually cued trials, temporal cortex for aurally cued trials, and minor non-localized differences in MEG activity for trials initiated from memory. The differential roles of visual and auditory sensory cortices during free and forced actions provided insight into the neural processing steps that were engaged to initiate cued actions. Specifically, it suggested that detectable differences exist in the activity of sensory cortices and their target sites when subjects performed free and forced actions in response to sensory cues.