Region Of Interest (ROI) Analysis in EEG Signals for Depression Detection using Machine Learning

Through a classification framework that analyses the rating systems of the different regions with regard to depression, this article attempts to explore the association amongst the brain regions and depression. Some of the domains showed higher categorization scores than others, which may indicate a connection between depression and those areas. When it comes to theta band power, both the temporal and parietal regions are crucial, while the frontal, occipital, and parietal areas are relevant for delta band power. Additionally, all areas are engaged in beta band power, but only the CENT and PAR sectors are active in alpha band power. Not only that, but classification accuracy is generally good in the parietal, central, and temporal regions, with the temporal region achieving promising results with fewer neurons than other regions. Increasing the number of networks in the temporal area may improve accuracy, according to the research. In addition, MAHF with SVM and ReliefF with SVM show promise for depression diagnosis because to their high classification accuracy. Finally, it’s worth noting that the findings indicate the importance of certain brain regions and performance in feature selection, which could help with depression diagnosis within the context of depression research.