Source-based artifact-rejection techniques for TMS–EEG

Tuomas P. Mutanen*, Johanna Metsomaa, Matilda Makkonen, Giuseppe Varone, Laura Marzetti, Risto J. Ilmoniemi

*Corresponding author for this work

Research output: Contribution to journalReview Articlepeer-review

11 Citations (Scopus)
84 Downloads (Pure)

Abstract

Neuronal electroencephalography (EEG) signals arise from the cortical postsynaptic currents. Due to the conductive properties of the head, these neuronal sources produce relatively smeared spatial patterns in EEG. We can model these topographies to deduce which signals reflect genuine TMS-evoked cortical activity and which data components are merely noise and artifacts. This review will concentrate on two source-based artifact-rejection techniques developed for TMS–EEG data analysis, signal-space-projection–source-informed reconstruction (SSP–SIR), and the source-estimate-utilizing noise-discarding algorithm (SOUND). The former method was designed for rejecting TMS-evoked muscle artifacts, while the latter was developed to suppress noise signals from EEG and magnetoencephalography (MEG) in general. We shall cover the theoretical background for both methods, but most importantly, we will describe some essential practical perspectives for using these techniques effectively. We demonstrate and explain what approaches produce the most reliable inverse estimates after cleaning the data or how to perform non-biased comparisons between cleaned datasets. All noise-cleaning algorithms compromise the signals of interest to a degree. We elaborate on how the source-based methods allow objective quantification of the overcorrection. Finally, we consider possible future directions. While this article concentrates on TMS–EEG data analysis, many theoretical and practical aspects, presented here, can be readily applied in other EEG/MEG applications. Overall, the source-based cleaning methods provide a valuable set of TMS–EEG preprocessing tools. We can objectively evaluate their performance regarding possible overcorrection. Furthermore, the overcorrection can always be taken into account to compare cleaned datasets reliably. The described methods are based on current electrophysiological and anatomical understanding of the head and the EEG generators; strong assumptions of the statistical properties of the noise and artifact signals, such as independence, are not needed.

Original languageEnglish
Article number109693
Pages (from-to)1-18
Number of pages18
JournalJournal of Neuroscience Methods
Volume382
DOIs
Publication statusPublished - 1 Dec 2022
MoE publication typeA2 Review article, Literature review, Systematic review

Keywords

  • Artifact signal
  • Electroencephalography
  • Noise
  • Signal space
  • Source space
  • Transcranial magnetic stimulation

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