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.

中文翻译：

---

ReMAE：一个用户友好的工具箱，用于从 EEG 中去除肌肉伪影

本文描述了一个用户友好的工具箱 ReMAE，用于从脑电图 (EEG) 中去除肌肉伪影，在 MATLAB 环境下运行。它实现了一系列最先进的方法，用于从文献中的 EEG 中去除肌肉伪影，并提供图形用户界面 (GUI)。根据现有研究的分类，该工具箱包含三种基于输入脑电通道数量的去噪模式，即多通道、单通道和少通道。此外，该工具箱将去噪方法模块化并可视化每个模块。这意味着用户可以很容易地观察每一步的详细去噪性能，甚至可以根据自己的理解设计定制的组合方法。在目前的文献中，由于肌肉伪影的复杂性，不存在适用于所有情况的方法。这项工作的主要动机是通过简单直观的 GUI 将神经科学家、心理学家和临床医生与成熟和前沿的方法联系起来，并鼓励他们在各种真实场景中广泛研究不同的方法。