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

Eqlimi, Ehsan; Bockstael, Annelies; Schönwiesner, Marc; Talsma, Durk; Botteldooren, Dick · 2023 · Crossref

DOI: 10.1111/ejn.16159

archive: archived pipeline: cataloged verified

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Summary

This study investigates how attentional engagement and memory encoding evolve over time during speech perception in noisy environments, addressing the challenge of auditory distractions in ecologically valid settings. While previous research often focused on identifying which speaker a listener attends to in "cocktail party" scenarios, this work examines the neural dynamics of attending to speech while suppressing background noise. The authors hypothesize that EEG microstate complexity and unpredictability reflect the cognitive effort required for this process, distinguishing between sensory processing and higher-level cognitive engagement. The experimental design involved 23 healthy adult participants who underwent three sequential listening tasks while their 64-channel EEG signals were recorded. Task 1 (LA) required attentive listening to English lectures presented with various background noises (multi-talker babble, fluctuating traffic, highway rumble) or inaudible pink noise. Tasks 2 and 3 (BA and BUA) involved attending to or ignoring fragments of the same background noises without speech. EEG data were preprocessed and analyzed using time-frequency methods and a novel spatio-temporal approach combining EEG microstate analysis with recurrence quantification analysis (RQA). This methodology allowed the researchers to quantify the time-varying complexity of brain states, specifically measuring microstate recurrence time, entropy, and determinism, as well as the alpha-to-theta power ratio. The results demonstrated that microstate complexity and unpredictability significantly increased when attention was directed toward speech compared to tasks involving only background noise (LA > BA & BUA). Notably, the time between the recurrence of microstates was significantly longer in the speech-attended condition, indicating that coping with background noise during speech comprehension demands sustained cognitive effort. The study identified a distinct two-stage time course for both microstate complexity and the alpha-to-theta power ratio. In early epochs, these metrics were lower, gradually increasing to reach a steady level in later epochs. This temporal progression suggests that the initial stage of listening is primarily driven by sensory processes and information gathering, while the subsequent stage involves higher-level cognitive engagement, including mnemonic binding and memory encoding. These findings provide a quantifiable neural estimate of information encoding quality, surpassing the limitations of subjective self-reports. The identification of a two-stage cognitive process offers insights into how the brain integrates sensory input with predictive coding and memory systems to handle auditory distractions. This understanding has significant implications for designing conducive learning environments, improving communication interfaces in artificial intelligence, and developing cognitively controlled hearing solutions for both hearing-impaired and normal-hearing individuals in noisy contexts.

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• auditory