Speech Tracking in Complex Auditory Scenes with Differentiated In- and Out-Field-Of-View Processing in Hearing Aids

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

“…Due to the fact that TRFs and the corresponding SR decoders are mathematically related (Haufe et al, 2014), it could be expected that both approaches would be similarly influenced by variations in SNR. Consistent with that, the presumed benefit in gain and thereby enhanced tracking reliably yields higher SR accuracies for selectively attended compared to unattended auditory streams (O'Sullivan et al, 2015, Fuglsang et al, 2017, Hausfeld et al, 2018, Schäfer et al, 2018, O'Sullivan et al, 2019, Alickovic et al, 2020, Teoh et al, 2022, Mai et al, 2022. Considering all evidences, it seems plausible to assume that the observed results in regression-based analyses of selective speech tracking may, at least partially, be attributed to the generation of speech-evoked ERPs including the N1 effect related to selective auditory attention.…”

“…In recent years, regression-based methods to study the relationship between continuous variables have been rapidly growing in popularity. Supported by the provision of freely available data processing toolboxes (Crosse et al, 2016, Brodbeck et al, 2022, the modeling approaches have led to remarkable insights into neural tracking and were explicitly applied in studies investigating, e.g., multimodal interactions (Crosse et al, 2015), music perception (Weineck et al, 2022), pitch processing (Brodbeck and Simon, 2022), linguistic processing (Gillis et al, 2021), speech intelligibility (Vanthornhout et al, 2018, Muncke et al, 2022, effects of hearing aid processing strategies (Alickovic et al, 2020, 2021, Mai et al, 2022 and selective attention (Schäfer et al, 2018, Teoh et al, 2022. In these frameworks, continuous relations may be linearly modeled in forward or backward direction.…”