Biology subjects

Smalt, C. J.

Publications and source records attributed to Smalt, C. J..

2 recordsLinked to original sources

bioRxiv2025

A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments

ObjectiveThere is significant research in accurately determining the focus of a listeners attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listeners focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listeners attention to the target speaker in real time and investigate the underlying neural bases of this improvement. ApproachThis paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listeners real-time attention decoding accuracy was used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding. ResultsIn this study, we found evidence of suppression of (i.e., reduction in) neural tracking of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.012). We did not find a statistically significant increase in the neural tracking of the attended talker. SignificanceThese results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.

neuroscience↗

bioRxiv2021

EEG Alpha Power and Pupil Diameter Reflect Endogenous Auditory Attention Switching and Listening Effort

Auditory attention describes a listeners focus on an acoustic source while they ignore other competing sources that might be present. In an environment with multiple talkers and background noise (i.e. the cocktail party effect), auditory attention can be difficult, requiring the listener to expend measurable cognitive effort. A listener will naturally interrupt sustained attention on a source when switching towards another source during conversation. This change in attention is potentially even more taxing than maintaining sustained attention due to the limits of human working memory, and this additional effort required has not been well studied. In this work, we evaluated an attention decoder algorithm for detecting the change in attention and investigated cognitive effort expended during attentional switching and sustained attention. Two variants of endogenous attention switching were explored: the switches either had in-the-moment decision making or a pre-defined attentional switch time. A least-squares, EEG-based, attention decoding algorithm achieved 64.1% accuracy with a 5-second correlation window and illustrated smooth transitions in the attended talker prediction through switches in sustained attention at approximately half of the analysis window size (2.2 seconds). The expended listening effort, as measured by simultaneous electroencephalography (EEG) and pupillometry, was also a strong indicator of switching. Specifically, centrotemporal alpha power [F(2, 18) = 7.473, P = 0.00434] and mean pupil diameter [F(2, 18) = 9.159, P = 0.0018] were significantly different for trials that contained a switch in comparison to sustained trials. We also found that relative attended and ignored talker locations modulate the EEG alpha topographic response. This alpha lateralization was found to be impacted by the interaction between experimental condition and whether the measure was computed before or after the switch [F(2,18) = 3.227, P = 0.0634]. These results suggest that expended listening effort is a promising feature that should be pursued in a decoding context, in addition to speech and location-based features.

neuroscience↗