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Aging is associated with an exaggerated representation of the speech envelope in auditory cortex. The relationship between this age-related exaggerated response and a listener’s ability to understand speech in noise remains an open question. Here, information-theory-based analysis methods are applied to magnetoencephalography recordings of human listeners, investigating their cortical responses to continuous speech, using the novel nonlinear measure of phase-locked mutual information between the speech stimuli and cortical responses. The cortex of older listeners shows an exaggerated level of mutual information, compared with younger listeners, for both attended and unattended speakers. The mutual information peaks for several distinct latencies: early (∼50 ms), middle (∼100 ms), and late (∼200 ms). For the late component, the neural enhancement of attended over unattended speech is affected by stimulus signal-to-noise ratio, but the direction of this dependency is reversed by aging. Critically, in older listeners and for the same late component, greater cortical exaggeration is correlated with decreased behavioral inhibitory control. This negative correlation also carries over to speech intelligibility in noise, where greater cortical exaggeration in older listeners is correlated with worse speech intelligibility scores. Finally, an age-related lateralization difference is also seen for the ∼100 ms latency peaks, where older listeners show a bilateral response compared with younger listeners’ right lateralization. Thus, this information-theory-based analysis provides new, and less coarse-grained, results regarding age-related change in auditory cortical speech processing, and its correlation with cognitive measures, compared with related linear measures. NEW & NOTEWORTHY Cortical representations of natural speech are investigated using a novel nonlinear approach based on mutual information. Cortical responses, phase-locked to the speech envelope, show an exaggerated level of mutual information associated with aging, appearing at several distinct latencies (∼50, ∼100, and ∼200 ms). Critically, for older listeners only, the ∼200 ms latency response components are correlated with specific behavioral measures, including behavioral inhibition and speech comprehension. 

In the last few years, a large number of experiments have been focused on exploring the possibility of using non-invasive techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), to identify auditory-related neuromarkers which are modulated by attention. Results from several studies where participants listen to a story narrated by one speaker, while trying to ignore a different story narrated by a competing speaker, suggest the feasibility of extracting neuromarkers that demonstrate enhanced phase locking to the attended speech stream. These promising findings have the potential to be used in clinical applications, such as EEG-driven hearing aids. One major challenge in achieving this goal is the need to devise an algorithm capable of tracking these neuromarkers in real-time when individuals are given the freedom to repeatedly switch attention among speakers at will. Here we present an algorithm pipeline that is designed to efficiently recognize changes of neural speech tracking during a dynamic-attention switching task and to use them as an input for a near real-time state-space model that translates these neuromarkers into attentional state estimates with a minimal delay. This algorithm pipeline was tested with MEG data collected from participants who had the freedom to change the focus of their attention between two speakers at will. Results suggest the feasibility of using our algorithm pipeline to track changes of attention in near-real time in a dynamic auditory scene.