Advanced Artifact Removal for Automated TMS-EEG Data Processing
Christopher C. Cline, Molly V. Lucas, Yinming Sun, Matthew Menezes, Amit Etkin
Abstract
Transcranial magnetic stimulation (TMS) with concurrent electroencephalography (EEG) is a key tool towards non-invasively characterizing causal circuits in humans. However, the recorded data is highly noisy due to a variety of stimulation-induced artifacts and other noise sources. While many TMS-EEG processing pipelines require manual intervention, the pipeline described here is fully automated. As part of this pipeline, we introduce several novel approaches specifically designed to mitigate TMS-induced artifacts to produce cleaner and more reliable TMS-evoked EEG responses.
Topics & Concepts
ElectroencephalographyArtifact (error)Pipeline (software)Computer scienceTranscranial magnetic stimulationNoise (video)Artificial intelligenceSpeech recognitionStimulationNeurosciencePsychologyProgramming languageImage (mathematics)EEG and Brain-Computer InterfacesNeural dynamics and brain functionAdvanced Memory and Neural Computing