MCEEGNet: a multi-cue EEG network for quantitative assessment of depression using emotional stimuli-induced EEG signals

Depression significantly affects health, manifesting as alterations in typical emotional responses. Its diagnosis depends on subjective evaluations by clinicians, which are often time-intensive. Electroencephalogram (EEG) signals offer a viable solution for aiding diagnosis through computational means. However, current methods primarily focus on binary classification of depression, neglecting the quantification of depression risk. We propose the Multi-Cue EEG Network (MCEEGNet), which consists of parallel branches of EEGNet that extract features from various emotional stimuli to approximate scores on the Patient Health Questionnaire (PHQ-9). MCEEGNet aims to identify depression in patients using EEG signals and assess the severity of the condition. Our method achieved 91.13% accuracy in classification and reported Mean Squared Error (MSE) and Mean Absolute Error (MAE) of 20.45 and 3.28, respectively, in the Multi-Emotion Induced EEG Depression Database. The experimental outcomes suggest that MCEEGNet is highly effective in diagnosing subthreshold depression, offering a comprehensive system for evaluating clinical depression through EEG analysis influenced by multiple emotional cues, thereby meeting the need for quantitative depression evaluation.