Emotion-induced higher wavelet entropy in the eeg with depression during a cognitive task

This paper presents a study about how emotion influences cognition. We used wavelet entropy as a tool to analyze event-related electroencephalograph during a cognitive task. Emotion and cognition are two major aspects of human mental life that are widely regarded as distinct but interacting. However, the mechanism of this interacting is still not well known. In our study, a recognition task with facial stimuli was utilized in order to address the influence of emotion on working memory. Three expressions of each face (happy-positive, sad-negative, and neutral) were chosen for the experiments. Since depression is characterized as a typical mental disease with emotion processing deficits, sixteen patients with depression and sixteen normal controls were chosen to participate in the experiment. The repeated measure analysis of variance (ANOVA) revealed that the patients with depression had a significantly higher entropies than the normal control overall the brain regions. Although behavior results did not indicate any emotion effect, wavelet entropy told more about it. The emotion effect was found in the right anterior and right center of the brain by the analysis of entropy. We concluded that patients with depression showed much higher emotion-induced disorder than normal persons after about 300 ms after stimulus onset. In methodology Wavelet entropy can help us to understand the interaction between emotion and cognition.