Research and selection of the optimal neural network architecture and parameters for depression classification using harmonized datasets

This paper presents the results of a study of the optimal architecture of a neural network for depressive disorder recognition based on harmonized EEG datasets. Based on the re-sults of the comparison, two architectures were chosen: EEGNet and a two-dimensional fully-convolutional neural network. Using the Bayesian optimization method, the optimal model parameters were selected to achieve maximum accuracy on the test samples. The average accuracy of depressive disorder classification on the test set using cross-validation was 0.68.