A Competitive Framework for Modeling EEG Microstate Durations

Background. This study examines a competition based model (Cmodel) designed to capture the temporal dynamics of successive brain microstates derived from electroencephalography (EEG) recordings during eyes-open conditions. The analyzed data were obtained from a public repository comprising microstate sequences from 60 sessions of a single subject . When applied to microstate dynamics, the Cmodel posits a stochastic competition among neural circuits underlying the expression of individual microstates. Methods.The model is formulated at a conceptual level (computational level in Marr framework) and employs a geometric distribution to account for the long right tail of microstate duration distributions, interpreted as the probability of failure of the currently active microstate to persist. To account for the short-lived left tail, the model incorporates a transient increase in the stability of the currently active network, or equivalently, a temporary decrease in the activation probability of competing microstates (refractory period). Results. The model provides a good fit to the microstate duration distributions across all 60 sessions. One third of sessions showed microstate identity sequential dependency with respect to the previous microstates. Discussion. These results suggest that the Cmodel captures key aspects of microstate temporal structure. Moreover, because microstate probabilities can be modulated by psychophysiological conditions, including the influence of previously active networks, the model may serve as a building block for more comprehensive neurobiological frameworks of neural and behavioral dynamics. In such frameworks, microstate sequences could emerge from structured competition and flow among neural networks supporting microstate expression.