Yang Qihang, Zhang Xuan, Chen Badong
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:510-513. doi: 10.1109/EMBC44109.2020.9176738.
Electroencephalography (EEG) based Brain Computer Interface (BCI) attracts more and more attention. Motor Imagery (MI) is a popular one among all the EEG paradigms. Building a subject-independent MI EEG classification procedure is a main challenge in practical applications. Recently, Convolutional Neural Network (CNN) has been introduced and achieved state-of-the-art performance in related areas. To extract subject-independent features in MI EEG classification, we propose the MI3DNet, using a remapped signal cubic as the input. Experiments show that MI3DNet has a higher performance with fewer parameters and layers. We also give a method to plot the parameters of the dense layer, and explain its effect.
基于脑电图（EEG）的脑机接口（BCI）越来越受到关注。运动想象（MI）是所有EEG范式中一种流行的方式。构建一个与个体无关的MI EEG分类程序是实际应用中的一个主要挑战。最近，卷积神经网络（CNN）已被引入并在相关领域取得了领先的性能。为了在MI EEG分类中提取与个体无关的特征，我们提出了MI3DNet，使用重新映射的信号立方体作为输入。实验表明，MI3DNet在参数和层数较少的情况下具有更高的性能。我们还给出了一种绘制密集层参数的方法，并解释其效果。
