Soft fusion of channel information in depression detection using functional near-infrared spectroscopy

Abstract

To address the gap in fNIRS-based depression detection research concerning channel selection and information fusion, and to possibly provide recommendations for channel design to fNIRS device manufacturers, we propose a novel framework for depression detection using functional near-infrared spectroscopy (fNIRS) with optimized channel selection and fusion. Involving a sample of 80 participants (40 depressed, 40 healthy), we employed Phase Space Reconstruction (PSR) to capture neurovascular nonlinear dynamics from the fNIRS data. Using multi-objective optimization (MOMVO), we identified key channels in brain regions such as the Left Dorsolateral Prefrontal Cortex, Right Infraorbital Superior Frontal Gyrus, Right Dorsolateral Prefrontal Cortex, and Right Middle Frontal Gyrus. Our approach achieved depression detection rates of 96.1% under positive stimuli, 91.3% under neutral stimuli, and 98.0% under negative stimuli, surpassing comparative methods by 5% to 12%. This framework demonstrates potential for improving early depression detection and clinical applications.