Application of EEG microstates in Parkinson's disease

Electroencephalography (EEG) microstate analysis is a promising technique for detecting transient brain dynamics and identifying disease-specific biomarkers in Parkinson's disease (PD). By capturing subsecond fluctuations in brain activity with intrinsic high temporal resolution and robust test-retest reliability, this method has potential applications in early diagnosis, disease severity assessment, and therapeutic monitoring in PD. Integrating microstate analysis with artificial intelligence (AI) further enhances the accuracy of recognizing PD-specific brain activity patterns. However, challenges such as methodological variability, lack of standardization, and AI-related limitations, remain substantial barriers to clinical translation. This review systematically explores the application of EEG microstate analysis in PD, broadening insights into disease mechanisms and personalized therapeutic options. Furthermore, we discuss existing challenges, underscore the need for methodological standardization, and highlight future directions, including large-scale validation studies and the integration of explainable AI (XAI) approaches to enhance clinical applicability.