Linear modeling of brain activity during selective attention to continuous speech: the critical role of the N1 effect in event-related potentials to acoustic edges

Mai, Adrian; Hillyard, Steven A.; Strauss, Daniel J. · 2025 · Crossref

DOI: 10.1007/s11571-025-10289-z

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Summary

This study investigates the relationship between traditional event-related potentials (ERPs) and modern linear regression-based modeling techniques used to analyze cortical tracking of continuous speech. Specifically, it addresses whether speech-evoked ERPs, particularly the N1 component enhanced by selective attention (the N1 effect), contribute to the accuracy of stimulus reconstruction and temporal response function (TRF) models. The authors hypothesize that consistent attention-related enhancements of the N1 component improve the signal-to-noise ratio, thereby facilitating the neural tracking of attended speech streams in dual-speaker scenarios. The researchers re-analyzed a publicly available EEG dataset from a selective auditory attention task involving 18 participants. Participants focused on one of two competing speakers presented at ±60° azimuth in an anechoic environment. The study employed two primary analytical approaches. First, EEG data were segmented based on acoustic edges derived from speech onset envelopes to extract speech-evoked ERPs. Second, linear forward and backward modeling techniques were applied to estimate TRFs and perform stimulus reconstruction (SR). To quantify the consistency of ERP responses, the authors calculated wavelet phase synchronization stability (WPSS) in the theta band (4–8 Hz), which corresponds to the dominant spectral content of the N1 component. This allowed for a direct comparison between the stability of ERP waveforms and the decoding performance of the linear models. The results demonstrated a strong spatiotemporal similarity between the modeled TRFs and the extracted speech-evoked ERPs, both exhibiting clear P1-N1-P2 complexes. Crucially, the study found that stimulus reconstruction accuracies were significantly higher for attended speech streams compared to ignored ones. This improvement was driven by a consistent enhancement of the N1 component in the attended condition, which increased the signal-to-noise ratio. Furthermore, the WPSS analysis revealed that the stability of the N1 phase in the theta band correlated with SR performance, confirming that consistent attention-driven ERP enhancements facilitate accurate neural tracking. These findings establish a direct link between classical ERP components and contemporary linear modeling approaches in speech processing. The study concludes that the N1 effect is not merely an artifact of transient stimuli but plays a critical role in the cortical representation of continuous speech. By enhancing the signal-to-noise ratio through consistent N1 amplification, selective attention facilitates the higher-order processing of attended speech streams. This bridges the gap between event-related potential research and regression-based speech tracking, suggesting that the mechanisms underlying the N1 effect are fundamental to the brain's ability to track and reconstruct continuous auditory inputs.

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• attention selective divided