Designing and comparing cleaning pipelines for TMS-EEG data: a theoretical overview and practical example

Abstract

Combining transcranial magnetic stimulation (TMS) with electroencephalography (EEG) is growing in popularity as a method for probing the reactivity and connectivity of neural circuits in basic and clinical research. However, using EEG to measure the neural responses to TMS is challenging due to the unique artifacts introduced by combining the two techniques. In this paper, we overview the artifacts present in TMS-EEG data and the offline cleaning methods used to suppress these unwanted signals. We then describe how open science practices, including the development of open-source toolboxes designed for TMS-EEG analysis (e.g., TESA - the TMS-EEG signal analyser), have improved the availability and reproducibility of TMS-EEG cleaning methods. We provide theoretical and practical considerations for designing TMS-EEG cleaning pipelines and then give an example of how to compare different pipelines using TESA. We show that changing even a single step in a pipeline designed to suppress decay artifacts results in TMS-evoked potentials (TEPs) with small differences in amplitude and spatial topography. The variability in TEPs resulting from the choice of cleaning pipeline has important implications for comparing TMS-EEG findings between research groups which use different online and offline approaches. Finally, we discuss the challenges of validating cleaning pipelines and recommend that researchers compare outcomes from TMS-EEG experiments using multiple pipelines to ensure findings are not related to the choice of cleaning methods. We conclude that the continued improvement, availability, and validation of cleaning pipelines is essential to ensure TMS-EEG reaches its full potential as a method for studying human neurophysiology.HighlightsConcurrent TMS-EEG is challenging due to artifacts in the recorded signals.We overview offline methods for cleaning TEPs and provide tips on pipeline design.We use TESA to compare pipelines and show changing a single step alters TEPs.We discuss the challenges in validating pipelines for TMS-EEG analysis.We suggest using multiple pipelines to minimise the impact of method choice on TEPs.

TMS-EEG shows mindfulness meditation is associated with an altered excitation/inhibition balance in the dorsolateral prefrontal cortex

“…For further information on the TMS-EEG data analysis pipeline used in the current, please see: (Rogasch et al, 2017). An example TMS-EEG analysis pipeline similar to the present study is provided at: https://nigelrogasch.gitbooks.io/tesa-user-manual/content/example_pipelines.html (Rogasch, 2017).…”

Neural effects of TMS trains on the human prefrontal cortex

“…TMS-EEG preprocessing was performed with version 2 of the AARATEP pipeline 36 , with source code available at github.com/chriscline/AARATEPPipeline. Data were processed in batches grouped by 4 sequential blocks (each batch containing probe responses from 48 real trains and 48 sham trains) to account for artifact changes that may occur over the duration of one session 37 . Epochs were extracted from 350 ms before to 1100 ms after each TMS probe pulse.…”

An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation

“…However, ICA can be subjective in its implementation by individual researchers, and the method may not be ideal for the analysis of specific types of data 66,67 . In particular, substantial attention has been paid to the need to remove the TMS artifact from TMS-EEG data 68–70 . Therefore, we performed a second analysis with delay differential analysis (DDA) 71–73 , a non-linear signal processing technique that requires minimal pre-processing and is noise insensitive 74,75 .…”