Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Distraction caused by auditory noise poses a considerable challenge to the quality of information encoding in speech comprehension. The aim of this study was to explore the temporal dynamics and complexity of electroencephalog-raphy (EEG) microstates in relation to attentional engage-ment over time, contributing to the understanding of speech perception in noise. We examined three listening condi-tions: speech perception with background noise, focused attention on the background noise, and intentional disre-gard of the background noise. Our findings revealed an increase in complexity during the transition of microstates and a slower microstate recurrence when individuals directed their attention to speech compared to tasks without speech. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the ini-tial stage is primarily driven by sensory processes and infor-mation gathering, while the second stage involves higher-level cognitive engagement, including mnemonic binding and memory encoding.