Empirical mode decomposition for sleep-wake analysis in a corticothalamic neural field model

Corticothalamic oscillations play a crucial role in regulating sleep and wake states, yet their underlying mechanisms remain poorly understood. Traditional spectral analysis methods often struggle to capture nonlinear, non-stationary neural oscillations. In this study, we apply iterated masking empirical mode decomposition (itEMD) to analyze simulated electroencephalographic (EEG) signals generated by a physiologically-based corticothalamic neural field model, which replicates sleep and wake dynamics. The algorithm iteratively decomposes EEG signals into intrinsic mode functions (IMFs), providing a fine-grained characterization of frequency components associated with different vigilance states. Our results reveal distinct oscillatory patterns in sleep and wake states, including dominant low-frequency rhythms during sleep and broadband activity in wakefulness. By comparing model-derived IMFs with characteristic features of EEG observed in sleep studies, we provide insights into the dynamical structure of corticothalamic oscillations and their role in state transitions. This study demonstrates the potential of itEMD as a powerful tool for analyzing simulated brain signals and provides a framework for linking computational models to empirical sleep studies.Clinical relevance- This study presents a novel approach that could enhance EEG-based diagnostics, sleep staging, seizure prediction, and personalized treatment strategies, particularly for sleep disorders, epilepsy, and neurodegenerative diseases.