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In this article, we present a new EEG signal classification framework byintegrating the complex-valued and real-valued Convolutional NeuralNetwork(CNN) with discrete Fourier transform (DFT). The proposed neural networkarchitecture consists of one complex-valued convolutional layer, tworeal-valued convolutional layers, and three fully connected layers. Our methodcan efficiently utilize the phase information contained in the DFT. We validateour approach using two simulated EEG signals and a benchmark data set andcompare it with two widely used frameworks. Our method drastically reduces thenumber of parameters used and improves accuracy when compared with the existingmethods in classifying benchmark data sets, and significantly improvesperformance in classifying simulated EEG signals.

A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)