论文标题
与脑电图应用程序（EEG）应用的混合复合物值的神经网络框架
A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)
论文作者
论文摘要
在本文中，我们通过整合具有离散的傅立叶变换（DFT）的复杂值和实价卷积神经网络（CNN）来提出一个新的EEG信号分类框架。所提出的神经网络架构由一个复杂值值的卷积层，两个实值卷积层和三个完全连接的层组成。我们的方法可以有效利用DFT中包含的相位信息。我们使用两个模拟的EEG信号和一个基准数据集验证我们的方法，并将其与两个广泛使用的框架进行比较。与对基准数据集进行分类的现有方法相比，我们的方法大大减少了所使用的参数的数量并提高了准确性，并显着提高了对模拟的EEG信号进行分类的性能。
In this article, we present a new EEG signal classification framework by integrating the complex-valued and real-valued Convolutional Neural Network(CNN) with discrete Fourier transform (DFT). The proposed neural network architecture consists of one complex-valued convolutional layer, two real-valued convolutional layers, and three fully connected layers. Our method can efficiently utilize the phase information contained in the DFT. We validate our approach using two simulated EEG signals and a benchmark data set and compare it with two widely used frameworks. Our method drastically reduces the number of parameters used and improves accuracy when compared with the existing methods in classifying benchmark data sets, and significantly improves performance in classifying simulated EEG signals.
