Resting EEG Features and Their Application in Depressive Disorders

Abstract

This research is aimed to analysis the resting EEG features in depression and the application in clinic. Sixteen patients with depression and sixteen healthy controls were involved in this study. Both features from the alpha and beta frequency bands were selected to analysis in this study. First the features' sensitivity to the group-difference and the correlation to the clinical HAMD scale score were analyzed, and then the classification method was used to further test the role of the resting EEG features in depression. The results showed that the difference between depression and healthy controls in the absolute power of beta band in the left prefrontal lobe was significant. And the alpha left-right asymmetry in the prefrontal cortex had a correlation with HAMD scale score. In addition, the classification based on the features showed that there was a relative higher accuracy rate to identify the depressions than to identify the healthy controls. Specifically, the classification based on alpha asymmetry was higher than that based on beta asymmetry, and the absolute power in beta band was higher than that in alpha band. Alpha asymmetry is a traditional sensitive resting EEG features for depression, this study provide new evidence to support the view. The findings here further suggest that absolute power in beta band would be important biomarker in depression.