Comparing Resting State And Task-based Eeg Using Machine Learning To Predict Vulnerability To Depression In A Non-clinical Population

This article explores the efficacy of using both resting state EEG (rs-EEG) and task-based EEG data to understand and predict Major Depressive Disorder (MDD). Forty participants volunteered for the study and questionnaires and EEG data were collected from them. Results showed that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. A Long Short Term Memory model and a 1D-Convolution neural network were used to predict vulnerability to depression and the latter gave the maximum accuracy of 98.06%.