Comparison of Three ICA Algorithms for Ocular Artifact Removal from TMS-EEG Recordings

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a powerful tool to investigate brain excitability and information processing in brain networks. However, EEG-TMS recordings are challenging because EEG is contaminated by powerful TMS-related artifacts. Because of these artifacts, different EEG-driven analyses (for instance, source analysis and analysis of information flow on the sensors and source level) reveal incorrect results. The aim of this study was to remove ocular artifacts from TMS-EEG recordings following stimulation of motor cortex using three independent component analysis (ICA) algorithms and to evaluate the effectiveness of these algorithms. We showed that the temporal ICA algorithm better separates those components that contain time-locked eye blink artifacts.