TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Concurrent electroencephalography (EEG) and transcranial magnetic stimulation (TMS) is crucial for brain research but faces signal processing challenges.
• Artifacts in TMS-EEG data often obscure TMS-evoked potentials (TEPs), limiting the technology's widespread use.
• The increasing complexity of data processing and multi-site data integration necessitates standardized methods for TEP recovery.

Purpose of the Study:

• To introduce TMSEEG, an open-source MATLAB application for streamlined TMS-EEG signal processing.
• To provide a standardized, step-by-step workflow for artifact removal and TEP recovery in TMS-EEG data.
• To facilitate the widespread utility and standardization of TMS-EEG technology in brain research.

Main Methods:

• Development of TMSEEG, an open-source MATLAB application with a modular design and graphical user interface (GUI).
• Implementation of algorithms for targeted removal of TMS-induced and general EEG artifacts.
• Integration with EEGLAB for seamless EEG signal processing and parameter configuration.

Main Results:

• TMSEEG offers a user-friendly, step-by-step workflow for processing TMS-EEG data.
• The application effectively removes artifacts and facilitates TEP recovery across various TMS protocols.
• Quality control checkpoints and visual feedback are provided throughout the processing pipeline.

Conclusions:

• TMSEEG is the first open-source GUI-based pipeline for TMS-EEG signal processing.
• This toolbox standardizes artifact removal and TEP extraction, promoting broader adoption of TMS-EEG.
• TMSEEG enhances the study of brain health and function by improving TMS-EEG data analysis.