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

Abstract

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings.

Intracortical Dynamics Underlying Repetitive Stimulation Predicts Changes in Network Connectivity

“…2F, right; Table 3) and HGP in the offset period. We next asked whether the offset response could be used as a proxy for the response during stimulation, as determining the response during stimulation in other modalities such as rTMS can be challenging because of the multitude of stimulation-related artifacts (Wu et al, 2018). Thus, we evaluated the relationships between the stimulation response and the offset response using linear regression.…”

“…Two studies have recorded scalp electroencephalogram (EEG) while applying noninvasive rTMS and reported changes in the evoked potential during stimulation (Hamidi et al, 2010;Veniero et al, 2010). However, because of the short latency of these evoked potentials and the possibility of residual stimulationrelated artifacts, the interpretation of these findings is limited (Rosanova et al, 2012;Wu et al, 2018). In contrast, invasive recordings provide high spatiotemporal resolution with temporally defined artifact, allowing precise measurement of neural activity after each pulse.…”

Intracortical dynamics underlying repetitive stimulation predicts changes in network connectivity

“…Two studies have recorded scalp electroencephalogram (EEG) while applying non-invasive rTMS and reported changes in the evoked potential during stimulation 10,11 . However, due to the short latency of these evoked potentials and the possibility of residual stimulation-related artifacts, the interpretation of these findings is limited 12,13 . In contrast, invasive recordings provide high spatiotemporal resolution with temporally defined artifact, allowing precise measurement of neural activity after each pulse.…”

“…The offset period showed a significant increase in low BLP (Fig 2D, paired t-test; t(13) = 6.06, P < 0.001) and mid BLP (t(13) = 7.15, P < 0.001), but not HGP. Further, low BLP was significantly higher than both mid BLP (Supplementary Fig 2, right panel, paired t-test, t(13) = 3.30, P = 0.006) and HGP (t(13) = 3.52, P = 0.004) in the offset period.We next asked if the offset response could be used as a proxy for the response during stimulation, as determining the response during stimulation in other modalities such as rTMS can be challenging due to the multitude of stimulation-related artifacts12 .Thus, we evaluated the relationships between the stimulation response and the offset response. Linear regression was performed using data points from all patients.…”

TMS-EEG: first steps towards a clinical application in epilepsy

“…These consist of Independent Component Analysis (ICA) , Principal Component Analysis (PCA) ) and other techniques ). To assist researchers, attempts have been made to create dedicated artifact rejection toolboxes (Atluri et al 2016;Wu et al 2018). …”