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

Abstract

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 electroencephalography (EEG) microstates in relation to attentional engagement over time, contributing to the understanding of speech perception in noise. We examined three listening conditions: speech perception with background noise, focused attention on the background noise, and intentional disregard 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 initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher-level cognitive engagement, including mnemonic binding and memory encoding.