This research paper proposes a new method for identifying Generalized Anxiety Disorder (GAD) using electroencephalography (EEG) microstate analysis. The authors integrated fast independent component analysis (FastICA) with microstate analysis to improve the spatial resolution and sensitivity of GAD detection. Summary of the Research The study addresses the challenge of diagnosing GAD, which currently relies on subjective patient reporting. While EEG microstate analysis shows promise in detecting GAD-related neural dynamics, its clinical application is limited by a lack of spatial resolution and sensitivity. To overcome this, the researchers developed a new framework that combines FastICA with microstate analysis. This approach enhances the spatial specificity of EEG signal decomposition, which allows for a more accurate identification of GAD. The method works by isolating dominant independent components and projecting them onto the channels with the highest weights. This reduces the "volume conduction effect" and signal mixing across channels, resulting in a clearer spatial topography of EEG microstates. The study involved 28 GAD patients and 28 healthy controls. The researchers found that the FastICA-enhanced microstate features showed significant differences between the two groups. Specifically, they observed an increased occurrence, coverage, and duration of a particular microstate (microstate A*) in GAD patients. Furthermore, a Support Vector Machine (SVM) classifier using these enhanced features showed improved sensitivity (a 3.6% increase) and precision (a 5.5% increase) compared to the standard microstate analysis approach.