Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures

Abstract

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field.

A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

“…In conclusion, our framework, based on a graph indices features and common ML classifiers, turn out to be a robust way to approach the challenge in PNES discrimination. In addition, in order to exploit brain state changing in PNES, we plan to apply this method to data acquired during transcranial magnetic stimulation (TMS) [ 67 ], as well to compute source modeling, to study and map how the brain networks of PNES change under external perturbations.…”

Signatures of Brain Network Alteration in Psychogenic Non-Epileptic Seizures: A Rest-EEG Study Based on Power Spectral Density and Phase Lag Index

“…Next, we plan to apply this method to data acquired during transcranial magnetic stimulation (TMS) [49] to study how the brain networks of PNES changes under external perturbations.…”

“…It could also be interesting to investigate whether the abnormal organization found in this study can be further confirmed in a large cohort of PNES subjects using other methods such as weighted PLI or directed PLI. Next, we plan to apply this method to data acquired during transcranial magnetic stimulation (TMS) [49] to study how the brain networks of PNES changes under external perturbations.…”

Effects of transcranial magnetic stimulation on neurobiological changes in Alzheimer's disease (Review)

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