Detecting high-frequency brain disorder signals using dynamic mode decomposition from EEG

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.

Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2608.02804 [q-bio.NC]

(or arXiv:2608.02804v1 [q-bio.NC] for this version)

https://doi.org/10.48550/arXiv.2608.02804 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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