Finding potential electroencephalography parameters for identifying clinical depression

Abstract: This master thesis report describes signal processing parameters of electroencephalography (EEG) signals with a significant difference between the signals from the animal model of clinical depression and the non-depressed animal model. The signal from the depressed model had a weaker power in gamma (30 - 80 Hz) than the non-depressed model during awake and it had a stronger power in delta (1.5 - 4 Hz) during sleep. The report describes the process of using visualisation to understand the shape of the signal which helps with interpreting results and helps with the development of parameters. A generic tool for time-frequency analysis was improved to cope with the size of the weeklong EEG dataset. A method for evaluating the quality of how well the EEG parameters are able to separate the strains with as short recordings as possible was developed. This project shows that it is possible to separate an animal model of depression from an animal model of non-depression based on its EEG and that EEG-classifiers may work as indicative classifiers for depression. Not a lot of data is needed. Further studies are needed to verify that the results are not overly sensitive to recording setup and to study to what extent the results are translational. It might be some of the EEG parameters with significant differences described here are limited to describe the difference between the two strains FSL and SD. But the classifiers have reasonable biological explanations that makes them good candidates for being translational EEG-based classifiers for clinical depression.