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Removing artifacts from TMS-evoked EEG: A methods review and a unifying theoretical framework
Julio C Hernandez-Pavon1, Dimitris Kugiumtzis2, Christoph Zrenner3
1Legs + Walking Lab, Shirley Ryan AbilityLab (Formerly The Rehabilitation Institute of Chicago), Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA.
Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is crucial for brain research but suffers from signal artifacts. This study reviews and unifies artifact removal methods, focusing on spatial filtering techniques like beamforming for improved data quality.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is a powerful tool for investigating cortical function.
- Artifacts in EEG signals significantly limit the clinical and research applications of TMS-EEG.
- Existing artifact removal methods include ICA, PCA, and SSP, with varying degrees of success.
Purpose of the Study:
- To provide a comprehensive review of theoretical and mathematical underpinnings of TMS-EEG artifact removal methods.
- To introduce beamforming as a unified framework for popular spatial filtering techniques.
- To enable comparative assessment of different artifact removal methods for diverse applications.
Main Methods:
- Review of existing literature on TMS-EEG artifact removal techniques.
- Mathematical formalization of spatial and temporal filtering approaches.
- Introduction of beamforming as a unifying framework for spatial filtering.
Main Results:
- Classification of artifact removal methods into spatial and temporal filters.
- Beamforming presented as a unified framework for assessing spatial filtering techniques.
- Comparative analysis of methods based on assumptions, challenges, and data applicability.
Conclusions:
- Effective artifact removal is critical for advancing TMS-EEG research and clinical use.
- The beamforming framework facilitates understanding and selection of appropriate spatial filtering methods.
- This work aids both non-mathematical and mathematical audiences in navigating TMS-EEG artifact removal strategies.