A quantitative physical model of the TMS-induced discharge artifacts in EEG

Abstract

The combination of Transcranial Magnetic Stimulation (TMS) with Electroencephalography (EEG) exposes the brain’s global response to localized and abrupt stimulations. However, large electric artifacts are induced in the EEG by the TMS, obscuring crucial stages of the brain’s response. Artifact removal is commonly performed by data processing techniques. However, an experimentally verified physical model for the origin and structure of the TMS-induced discharge artifacts, by which these methods can be justified or evaluated, is still lacking. We re-examine the known contribution of the skin in creating the artifacts, and outline a detailed model for the relaxation of the charge accumulated at the electrode-gel-skin interface due to the TMS pulse. We then experimentally validate implications set forth by the model. We find that the artifacts decay like a power law in time rather than the commonly assumed exponential. In fact, the skin creates a power-law decay of order 1 at each electrode, which is turned into a power law of order 2 by the reference electrode. We suggest an artifact removal method based on the model which can be applied from times after the pulse as short as 2 milliseconds onwards to expose the full EEG from the brain. The method can separate the capacitive discharge artifacts from those resulting from cranial muscle activation, demonstrating that the capacitive effect dominates at short times. Overall, our insight into the physical process allows us to accurately access TMS-evoked EEG responses that directly follow the TMS pulse, possibly opening new opportunities in TMS-EEG research.

EEG-based diagnostics of the auditory system using cochlear implant electrodes as sensors

“…electrode to be more sensitive to residual stimulation artifact or electrical artifacts caused by switch-off of the amplifier blanking compared to the larger MP2 plate electrode. Additionally, despite MP1 and MP2 being in similar locations, differences in the electrical characteristics of the surrounding tissue, such as skin and bone, can contribute to different discharge dynamics [Freche et al, 2018].…”

“…However, the artifact at 1 ms picked up by the MP1 electrode is absent when using MP2 as a ground. The MP2 ground electrode likely provides a nearby, low-impedance path for charges to quickly dissipate, which reduces the sensitivity of the MP1 electrode to electrical artifacts [Freche et al, 2018].…”

“…the weighted averaging procedure requires different weight ratios depending on stimulus intensity. This indicates that the effect of the electrode-tissue interface on the stimulus artifacts is non-linear, and depends strongly on the impedances and capacitive characteristics of the surrounding tissue [Kent et al, 2015;Freche et al, 2018].…”

Variability of dynamic patterns of cortical excitability in schizophrenia: A test-retest TMS-EEG study

“…The TMS artifact typically dominates the initial response signal for about 100 milliseconds. We applied a newly developed artifact removal algorithm that can easily be automated and produces markedly clean EEG signals over a period beginning as early as 5 milliseconds following the TMS pulse [34].…”

EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss

“…Electroencephalography (EEG) (Cecotti and Graser, 2011;Narizzano et al, 2017;Freche et al, 2018) is one of the most important non-invasive neuroimaging modalities used in cognitive neuroscience research (Mullen et al, 2015;Mete et al, 2016;Luo et al, 2018b) and brain-computer interface (BCI) development (Ahn and Jun, 2015;Arnulfo et al, 2015;Sargolzaei et al, 2015;Kumar et al, 2017). However, EEG-based cognitive neuroscience and BCI fields currently face a bottleneck in that high sampling rate and high-sensitivity EEG amplifier hardware are extremely expensive and generally complicated to operate for collecting signals (Jiang et al, 2017).…”