Blind Source Separation-Embedded Electroencephalogram Microstate Trajectory Modeling for Generalized Anxiety Disorder Identification.

Generalized anxiety disorder (GAD) diagnosis remains challenging due to lacking reliable biomarkers. Electroencephalography(EEG) microstate analysis shows promise in detecting GAD-related neural dynamics, but its clinical application is limited by insufficient spatial resolution and sensitivity. To address this challenge, we propose a novel framework integrating fast independent component analysis (FastICA) with microstate analysis to enhance spatial specificity in EEG signal decomposition,consequently, GAD can be more accurately identified.By isolating dominant independent components and projecting them onto the channels with the highest weights, our method effectively reduces volume conduction effects and signal mixing across channels, thereby sharpening the spatial topography of EEG microstates and improving spatial resolution. In a cohort of 28 GAD patients and 28 healthy controls, the FastICA-enhanced microstate features exhibited stronger intergroup differences in key parameters-including significantly increased occurrence, coverage, and duration of microstate A*-and revealed altered transition probabilities (e.g., C*→B*, p = 0.045), indicating improved discriminative power for anxiety-specific patterns.Furthermore, classification using a Support Vector Machine (SVM) with enhanced features achieved improved sensitivity (3.6% increase) and precision (5.5% increase) compared to the standard microstate approach.These findings underscore the potential of blind source separation techniques to refine EEG-based biomarkers for anxiety disorders. Our work not only advances the technical resolution of microstate analysis but also provides a clinically translatable pathway for objective GAD diagnosis.Future studies could extend this framework to broader psychiatric conditions and explore multimodal machine learning models for enhanced robustness.