EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search

Abstract

Abstract Objective. Major depressive disorder (MDD) is a prevalent psychiatric disorder whose diagnosis relies on experienced psychiatrists, resulting in a low diagnosis rate. As a typical physiological signal, electroencephalography (EEG) has indicated a strong association with human beings’ mental activities and can be served as an objective biomarker for diagnosing MDD. Approach. The basic idea of the proposed method fully considers all the channel information in EEG-based MDD recognition and designs a stochastic search algorithm to select the best discriminative features for describing the individual channels. Main results. To evaluate the proposed method, we conducted extensive experiments on the MODMA dataset (including dot-probe tasks and resting state), a 128-electrode public EEG-based MDD dataset including 24 patients with depressive disorder and 29 healthy controls. Under the leave-one-subject-out cross-validation protocol, the proposed method achieved an average accuracy of 99.53% in the fear-neutral face pairs cued experiment and 99.32% in the resting state, outperforming state-of-the-art MDD recognition methods. Moreover, our experimental results also indicated that negative emotional stimuli could induce depressive states, and high-frequency EEG features contributed significantly to distinguishing between normal and depressive patients, which can be served as a marker for MDD recognition. Significance. The proposed method provided a possible solution to an intelligent diagnosis of MDD and can be used to develop a computer-aided diagnostic tool to aid clinicians in early diagnosis for clinical purposes.