论文标题
Exbrabable：用于基于CNN的EEG解码和模型解释的开源GUI
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
论文作者
论文摘要
我们已经开发了图形用户界面（GUI），可吸引，专用于脑电图（EEG）解码中的卷积神经网络（CNN）模型训练和可视化。可用的功能包括模型训练，评估和参数可视化，以时间和空间表示。我们使用经过精心培训的电动机脑电图的公共数据集证明了这些功能，并将结果与现有的神经科学知识进行了比较。令人难以置信的主要目的是为调查人员提供快速，简化和用户友好的EEG解码解决方案，以利用大脑/神经科学研究中的尖端方法。
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-imagery EEG and compare the results with existing knowledge of neuroscience. The primary objective of ExBrainable is to provide a fast, simplified, and user-friendly solution of EEG decoding for investigators across disciplines to leverage cutting-edge methods in brain/neuroscience research.
