🤖 AI Summary
This study addresses the challenge of effectively identifying high-frequency dynamic signals in electroencephalography (EEG) that are associated with neurological disorders. For the first time, dynamic mode decomposition (DMD) is applied to analyze high-frequency EEG components. By extracting stable and consistent high-frequency dynamic features from neurologically relevant channels, the authors construct discriminative feature representations through a pipeline integrating principal component analysis (PCA), statistical testing based on random distribution assumptions, and machine learning–based classification. Experimental results reveal significant and stable high-frequency dynamic patterns in approximately 70% of the samples, which successfully differentiate individuals with alcohol dependence from healthy controls, thereby demonstrating the method’s effectiveness and novelty.
📝 Abstract
Recent studies have reported clearly identifiable dynamical changes in the high-frequency range of EEG signals recorded during specific stimuli, such as visual or auditory inputs, or in cases of brain disorders like epileptic seizures. In this study, we utilized Dynamic Mode Decomposition (DMD) to extract consistent and persistent dynamical changes in the high-frequency band from the signals of neurologically relevant EEG channels. High-frequency DMD modes were employed as features, composing a feature table. Through post-processing, a random distribution test was performed, revealing that approximately 70% of the samples exhibited consistent high-frequency dynamics within the signal of a specific channel. Furthermore, classification experiments confirmed that the PCA components of the feature table that passed the test formed a consistent pattern that distinguished the alcohol-dependent group from the control group.