Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software

Abstract

The concurrent use of transcranial magnetic stimulation with electroencephalography (TMS-EEG) is growing in popularity as a method for assessing various cortical properties such as excitability, oscillations and connectivity. However, this combination of methods is technically challenging, resulting in artifacts both during recording and following typical EEG analysis methods, which can distort the underlying neural signal. In this article, we review the causes of artifacts in EEG recordings resulting from TMS, as well as artifacts introduced during analysis (e.g. as the result of filtering over high-frequency, large amplitude artifacts). We then discuss methods for removing artifacts, and ways of designing pipelines to minimise analysis-related artifacts. Finally, we introduce the TMS-EEG signal analyser (TESA), an open-source extension for EEGLAB, which includes functions that are specific for TMS-EEG analysis, such as removing and interpolating the TMS pulse artifact, removing and minimising TMS-evoked muscle activity, and analysing TMS-evoked potentials. The aims of TESA are to provide users with easy access to current TMS-EEG analysis methods and to encourage direct comparisons of these methods and pipelines. It is hoped that providing open-source functions will aid in both improving and standardising analysis across the field of TMS-EEG research.

Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions: A position paper

“…Electrical artifacts reduction algorithms have been proposed for TMS-EEG (Ilmoniemi and Kicic, 2010, Vernet and Thut, 2014, see also Rogasch et al, 2016: https://nigelrogasch.github.io/TESA and Herring et al, 2015: www.fieldtriptoolbox.org/tutorial/tms-eeg for removal pipelines implemented in EEGlab and FieldTrip), as well as for TACS-EEG (Helfrich et al, 2014b, for limitations see Helfrich et al, 2014a) and TACS-MEG applications (Soekadar et al, 2013a, Neuling et al, 2015). For TMS-EEG, a recent study could convincingly show that provided appropriate artifact reduction procedures are followed, TMS-evoked potentials are absent from EEG in patients with extensive cortical lesions when damaged tissue is stimulated, but intact when the functional portion of cortex was targeted (Gosseries et al, 2015), suggesting that electrical artifacts can effectively be eliminated by existing procedures.…”

Impact of different intensities of intermittent theta burst stimulation on the cortical properties during TMS‐EEG and working memory performance

“…EEG data were analysed offline using EEGLAB (Delorme & Makeig, ), FieldTrip (Oostenveld, Fries, Maris, & Schoffelen, ), TESA (Rogasch et al, ) and custom scripts on Matlab platform (R2015b, The MathWorks, USA). For TMS‐EEG, data were epoched around the test TMS pulse (–1,000 to 1,000 ms), baseline corrected to the TMS‐free data (–500 to −50 ms), and data around the large signal from TMS pulse (–5 to 10 ms) were removed and linearly interpolated.…”

“…All data were bandpass filtered (second‐order, zero‐phase, Butterworth filter, 1–80 Hz) and bandstop filtered (48–52 Hz; to remove 50 Hz line noise) and epochs were inspected again to remove any anomalous activity in the EEG trace. The second round of FastICA was conducted, and additional artefactual components were removed based on a previous study (Rogasch et al, ) and using TESA toolbox as a guide (Rogasch et al, ). Components representing the following artefacts were removed; eye blinks and saccades (mean absolute z score of the two electrodes larger than 2.5), persistent muscle activity (high frequency power that is 60% of the total power), decay artefacts and other noise‐related artefacts (one or more electrode has an absolute z score of at least 4).…”

Effects of antiepileptic drugs on cortical excitability in humans: A TMS‐EMG and TMS‐EEG study

“…EEG data were analyzed blind to experimental conditions using MATLAB and the Fieldtrip open source toolbox (http://www.ru.nl/fcdonders/fieldtrip; [Oostenveld, Fries, Maris, & Schoffelen, ]), and in accordance with established artifact removal pipelines (Herring, Thut, Jensen, & Bergmann, ; Rogasch et al, ). Raw data were initially segmented into longer epochs from 1.5 s before to 1.5 s after the TMS pulse to avoid filter artifacts before later reducing segments to the actual epoch of interest (i.e., from −100 ms to 300 ms after the TMS pulse).…”