Comparative analysis of transfer learning performance on generalised EEG data for use in a depression diagnosis task

Abstract

The purpose of this work was to analyse the performance of different deep learning methods in the task of depression diagnosis based on bioelectrical brain activity data. In particular, to study the potential of transfer learning using an artificial neural network trained on a significant amount of “generalised” electroencephalography data in the task of diagnosing depression from non-invasive electroencephalogram signals. Methods. Deep learning approaches such as transfer learning and contrastive learning were used in the present study. Artificial neural networks were trained on the public HBN EO/EC task dataset containing recordings of electroencephalogram signals. The 1D CNN and EEGNet architectures were used as auxiliary artificial networks for transfer learning. In order to test the quality of contrastive learning, the dataset was augmented and the following algorithms were selected as the donor network: SimCLR, MoCo, NNCLR, BarlowTwins, DINO. Results. It was found that the EEGNet architecture used as a auxiliary network, due to its small size, does not give the full potential of contrastive learning algorithms. Therefore, EEGNet was replaced by a 1D CNN architecture with a larger number of parameters, which led to an increase in the quality performance of the models. Conclusion. Although the considered method of transient learning looks promising, the specificity of electroencephalogram signals and problems solved on their basis requires large-scale adaptation of algorithms and contrastive optimisation techniques for effective training of the target task. It is also worth noting the crucial role of the representativeness of the data set for training the donor network, since it is the completeness of real observations that increases the effectiveness of augmentation, which leads to an increase in the number of “useful” features in the latent space of the network and the best conditions for transfer learning in the target task. If we talk about the diagnosis of depression, the data should maximally represent examples of electroencephalograms of depressed patients.  

Keywords

• diagnosis
• depression
• electroencephalography
• artificial neural network
• transfer learning
• contrastive learning