  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.
