论文标题
EEGSIG：用于脑电信号处理的开源机器学习工具箱
EEGsig: an open-source machine learning-based toolbox for EEG signal processing
论文作者
论文摘要
为了实现全面的脑电图信号处理框架，我们在本文中演示了一个工具箱和图形用户界面eegsig，以实现EEG信号的完整过程。我们的目标是为EEG信号处理提供全面的套件，免费和开源的框架，其中尤其是没有编程经验的用户可以专注于他们的实际要求以加快医疗项目的速度。在MATLAB软件上开发的，我们汇总了所有三个EEG信号处理步骤，包括预处理，特征提取和分类为EEGSIG。除了多种有用的功能列表之外，在EEGSIG中，我们还实施了三种流行的分类算法（K-NN，SVM和ANN）来评估功能的性能。我们的实验结果表明，我们用于脑电图信号处理的新框架在不同的机器学习分类器算法下获得了出色的分类结果，并具有提取鲁棒性。此外，在EEGSIG中，为了选择提取的最佳功能，所有EEG信号通道都可以同时可见。因此，可以看到每个任务对信号的影响。我们认为，以用户为中心的MATLAB软件包是新手用户的令人鼓舞的平台，并为专家用户提供最高水平的控制
In the quest to realize a comprehensive EEG signal processing framework, in this paper, we demonstrate a toolbox and graphic user interface, EEGsig, for the full process of EEG signals. Our goal is to provide a comprehensive suite, free and open-source framework for EEG signal processing where the users especially physicians who do not have programming experience can focus on their practical requirements to speed up the medical projects. Developed on MATLAB software, we have aggregated all the three EEG signal processing steps, including preprocessing, feature extraction, and classification into EEGsig. In addition to a varied list of useful features, in EEGsig, we have implemented three popular classification algorithms (K-NN, SVM, and ANN) to assess the performance of the features. Our experimental results demonstrate that our novel framework for EEG signal processing attained excellent classification results and feature extraction robustness under different machine learning classifier algorithms. Besides, in EEGsig, for selecting the best feature extracted, all EEG signal channels can be visible simultaneously; thus, the effect of each task on the signal can be visible. We believe that our user-centered MATLAB package is an encouraging platform for novice users as well as offering the highest level of control to expert users
