Abstract
Listeners differ in their ability to attend to a speech stream in the presence of a competing sound. Differences in speech intelligibility in noise cannot be fully explained by the hearing ability which suggests the involvement of additional cognitive factors. A better understanding of the temporal fluctuations in the ability to pay selective auditory attention to a desired speech stream may help in explaining these variabilities. In order to better understand the temporal dynamics of selective auditory attention, we developed an online auditory attention decoding (AAD) processing pipeline based on speech envelope tracking in the electroencephalogram (EEG). Participants had to attend to one audiobook story while a second one had to be ignored. Online AAD was applied to track the attention toward the target speech signal. Individual temporal attention profiles were computed by combining an established AAD method with an adaptive staircase procedure. The individual decoding performance over time was analyzed and linked to behavioral performance as well as subjective ratings of listening effort, motivation, and fatigue. The grand average attended speaker decoding profile derived in the online experiment indicated performance above chance level. Parameters describing the individual AAD performance in each testing block indicated significant differences in decoding performance over time to be closely related to the behavioral performance in the selective listening task. Further, an exploratory analysis indicated that subjects with poor decoding performance reported higher listening effort and fatigue compared to good performers. Taken together our results show that online EEG based AAD in a complex listening situation is feasible. Adaptive attended speaker decoding profiles over time could be used as an objective measure of behavioral performance and listening effort. The developed online processing pipeline could also serve as a basis for future EEG based near real-time auditory neurofeedback systems.
Highlights
The human auditory system enables us to follow a speaker of interest among concurrent other speakers, even in noisy environments (Cherry, 1953)
We developed an online processing pipeline performing auditory attention decoding (AAD) on short segments of EEG data to detect the direction and level of attention in a two competing speaker paradigm
The implemented AAD method was combined with an adaptive 1-up, 1-down staircase procedure in order to optimize the trade-off between the duration of evaluation interval and the individual decoding performance
Summary
The human auditory system enables us to follow a speaker of interest among concurrent other speakers, even in noisy environments (Cherry, 1953). Hearing impaired and normal hearing listeners differ in their performance when they have to attend to a specific speech stream presented simultaneously with competing sounds (Bronkhorst, 2000; Kidd et al, 2007; Shinn-Cunningham and Best, 2008; Ruggles and Shinn-Cunningham, 2011). These performance differences in speech intelligibility in noise cannot be explained by the degree of hearing loss (Peissig and Kollmeier, 1997; Gallun et al, 2013; Glyde et al, 2013) and suggest the involvement of additional cognitive factors. As a consequence, hearing impaired individuals following a conversation in a complex listening situation may experience higher levels of effort to achieve optimal speech comprehension and may fatigue earlier compared to normal hearing controls (Kramer et al, 2006; Holman et al, 2019; Puschmann et al, 2019)
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