ReMAE: User-Friendly Toolbox for Removing Muscle Artifacts From EEG

This paper describes a user-friendly toolbox, ReMAE, for removing muscle artifacts from electroencephalogram (EEG), running under the MATLAB environment. It implements a series of state-of-the-art methods for muscle artifact removal from EEG in the literature, and provides a graphical user interface (GUI). According to the taxonomy of the existing studies, this toolbox contains three denoising modes based on the number of input EEG channels, i.e., multi-channel, single-channel, and few-channel. Furthermore, this toolbox modularizes the denoising methods and visualizes each module. This means that users can readily observe the detailed denoising performance in each step, and even design a customized combined method in terms of their own understanding. In the current literature, there exists no method applicable for all situations due to the complexity of muscle artifacts. The main motivation of this work is to connect neuroscientists, psychologists, and clinicians with both the well-established and cutting-edge methods through a simple and intuitive GUI, and encourage them to extensively investigate different methods in a variety of real scenarios.