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Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
Published on: May 12, 2014
TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction
Elias P Casula1, Alessandra Bertoldo2, Vincenza Tarantino3
1Non-invasive Brain Stimulation Unit, IRCCS Santa Lucia Foundation, Rome, Italy; Department of General Psychology, University of Padua, Padua, Italy; Sobell Department of Motor Neuroscience and Movement Disorders, University College London, London, United Kingdom.
A new adaptive detrend algorithm (ADA) effectively removes long-lasting decay artifacts in TMS-EEG recordings without altering TMS-evoked potentials. This method offers a reliable solution for analyzing brain activity after transcranial magnetic stimulation.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying brain function.
- A significant challenge in TMS-EEG is the long-lasting decay artifact (DA) generated by TMS, which obscures TMS-evoked potentials (TEPs).
- Existing artifact correction methods, such as independent component analysis (ICA), can inadvertently alter the TEP waveforms.
Purpose of the Study:
- To characterize the spatiotemporal impact of the TMS-induced decay artifact (DA) on EEG signals.
- To develop and validate a novel adaptive detrend algorithm (ADA) for accurate DA correction in TMS-EEG.
- To compare the efficacy of ADA against established ICA correction techniques.
Main Methods:
- Two experiments were conducted with 50 healthy volunteers.
- Experiment 1 compared the performance of ADA against two common ICA algorithms for DA correction.
- Experiment 2 assessed ADA's efficiency and its performance across different stimulation areas (frontal, motor, parietal).
Main Results:
- The DA was confirmed to affect EEG signals across both space and time.
- ADA successfully removed the DA without introducing significant alterations to the TEP waveforms.
- ICA-based corrections resulted in notable changes to the peak-to-peak amplitude of TEPs.
Conclusions:
- ADA provides a reliable and data-driven method for correcting TMS-induced decay artifacts.
- The algorithm preserves physiological responses and its effectiveness is independent of artifact characteristics or electrode count.
- ADA offers a superior alternative for artifact removal in TMS-EEG studies, ensuring the integrity of TEP analysis.
