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A recent study by Conde, Tomasevic et al. (2019) [1] puts a spotlight on the subtleties of experimental design and analysis of studies involving TMS-evoked EEG potentials (TEPs), specifically focusing on the challenge of disentangling genuine cortical responses to TMS from those resulting from concomitant sensory activation. This is a relevant topic that the TMSeEEG community has previously identified [2] and addressed with different strategies [3e6]. Based on the similarity of the evoked EEG responses they obtained in real TMS at different sites and in sham conditions (auditory and somatosensory scalp stimulation), the authors of [1] inferred that TEPs can be significantly contaminated by the effects of concurrent, non-transcranial stimulation.We acknowledge this is a valuable reminder to the TMS-EEG community; however, we contend that another fundamental implication of the work by Conde, Tomasevic and colleagues [1] -only incidentally mentioned at the end of their discussion e is that the evoked responses they obtain from both real TMS and sham conditions are substantially different from the TEPs reported in many of the previous studies (see, for example [7e11]). This discrepancy offers a timely opportunity to focus on the issue of the reproducibility of TEPs across laboratories and, most important, can encourage a constructive debate within the whole TMSeEEG community towards the optimization of shared procedures to obtain genuine responses to TMS.In this vein, Fig. 1 directly compares the TEPs reported in Ref.[1] with others previously published in different studies taken as a reference by Conde, Tomasevic and colleagues [1].The inspection of Fig. 1 clearly shows that it is possible to effectively trigger high-amplitude, sharply rising early (<50 ms) components and overall TEP wave-shapes that are specific for the angle and site of stimulation and that are very different from those obtained in Ref. [1]. This simple comparison highlights a general problem of reproducibility and offers an excellent opportunity to discuss two critical steps in TMSeEEG data acquisition: (i) maximising the impact of TMS on the cortex, and (ii) minimizing EEG confounding factors due to sensory co-stimulation.Regarding the impact of TMS on the cortex, it is very likely that the authors of [1] were not as effective as other investigators for the following reasons. First, they applied TMS with a maximum electric field (E-field) intensity between 70 and 90 V/m according to their estimation, assuming a priori that this would have warranted effective cortical activation based on a previous work [12]. However, in Ref. [1] the authors adopted a small coil (outer winding diameter: 45 mm) which, compared to the larger ones (outer winding

Background: Transcranial magnetic stimulation (TMS) evokes voltage deflections in electroencephalographic (EEG) recordings, known as TMS-evoked potentials (TEPs), which are increasingly used to study brain dynamics. However, the extent to which TEPs reflect activity directly evoked by magnetic rather than sensory stimulation is unclear.Objective: To characterize and minimize the contribution of sensory inputs to TEPs.Methods: Twenty-four healthy participants received TMS over the motor cortex using two different intensities (below and above cortical motor threshold) and waveforms (monophasic, biphasic). TMS was also applied over the shoulder as a multisensory control condition.Common sensory attenuation measures, including coil padding and noise masking, were adopted. We examined spatiotemporal relationships between the EEG responses to the scalp and shoulder stimulations at sensor and source levels. Furthermore, we compared three different filters (independent component analysis, signal-space projection with source informed reconstruction (SSP-SIR) and linear regression) designed to attenuate the impact of sensory inputs on TEPs. Results:The responses to the scalp and shoulder stimulations were correlated in both temporal and spatial domains, especially after ~60 ms, regardless of the intensity and stimuli waveform.Among the three filters, SSP-SIR showed the best trade-off between removing sensory-related signals while preserving data not related to the control condition. Conclusions:The findings demonstrate that TEPs elicited by motor cortex TMS reflect a combination of transcranially and peripherally evoked brain responses despite adopting sensory attenuation methods during experiments, thereby highlighting the importance of adopting sensory control conditions in TMS-EEG studies. Offline filters may help to isolate the transcranial component of the TEP from its peripheral component, but only if these components express different spatiotemporal patterns. More realistic control conditions may help to improve the characterization and attenuation of sensory inputs to TEPs, especially in early responses.

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Electroencephalographic (EEG) data is typically contaminated with non-neural artifacts which can confound the results of experiments. Artifact cleaning approaches are available, but often require time-consuming manual input and significant expertise. Advancements in artifact cleaning often only address a single artifact, are only compared against a small selection of pre-existing methods, and seldom assess whether a proposed advancement improves experimental outcomes. To address these issues, we developed RELAX (the Reduction of Electroencephalographic Artifacts), an automated EEG cleaning pipeline implemented within EEGLAB that reduces all artifact types. RELAX cleans continuous data using Multiple Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were tested using three datasets containing a mix of cognitive and resting recordings (N = 213, 60 and 23 respectively). RELAX was compared against six commonly used EEG cleaning approaches across a wide range of artifact cleaning quality metrics, including signal-to-error and artifact-to-residue ratios, measures of remaining blink and muscle activity, and the amount of variance explained by experimental manipulations after cleaning. RELAX with MWF and wICA_ICLabel showed amongst the best performance for cleaning blink and muscle artifacts while still preserving neural signal. RELAX with wICA_ICLabel (and no MWF) may perform better at detecting the effect of experimental manipulations on alpha oscillations in working memory tasks. The pipeline is easy to implement in MATLAB and freely available on GitHub. Given its high cleaning performance, objectivity, and ease of use, we recommend RELAX for data cleaning across EEG studies.