EEG-based depression classification using harmonized datasets

In this work harmonization technique was used to combine multiple depression-related datasets with EEG data into one. A simple CNN model was trained on each individual dataset and combined one to solve the same binary classification problem. Comparison of obtained metrics allowed to back up the necessity to combine data from different sources. Additionally, the inadequacy of employing regular shuffle split instead of per-patient split to the problem under consideration was demonstrated.