Carlton, L. B. | bioRxiv Science

bioRxiv2026

Decoding Spatial Attention in the Cocktail Party Problem Using Wearable Whole-head High-Density fNIRS

Spatial attention is critical for solving the cocktail party problem, a longstanding problem in neuroscience and artificial speech recognition. The ability to decode where humans are attending in a cocktail party like scene would empower applications in brain computer interfaces and assistive devices such as hearing aids. Here we demonstrate that, in an overt attention task, the attended spatial location can be decoded robustly from single trial hemodynamic responses, using a wearable whole head high density fNIRS system. We also identify critical brain regions that make the highest contribution to decoding accuracy. Specifically, we find that decoding based on a small fraction of channels within the left and right inferior parietal lobule (IPL), achieve maximal decoding accuracy comparable to all channels. These results open the way for the design of novel BCIs and assistive devices integrated with fNIRS, that can be steered by spatial attention.

neuroscience↗

bioRxiv2024

Investigating the variability of physiological response functions across individuals and brain regions in functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is a valuable neuroimaging tool for studying brain function and functional connectivity between brain regions. However, the blood oxygen level dependent (BOLD) signal used to generate the fMRI images can be influenced by various physiological factors, such as cardiac and respiratory activity. These physiological effects, in turn, influence the resulting functional connectivity patterns, making physiological noise correction a crucial step in the preprocessing of fMRI data. When concurrent physiological recordings are available, researchers often generate nuisance regressors to account for the effect of heart rate and respiratory variations by convolving physiological response functions (PRF) with the corresponding physiological signals. However, it has been suggested that the PRF characteristics may vary across subjects and different regions of the brain, as well as across scans of the same subject. To investigate the dependence of PRFs on these factors, we examine the performance of several different PRF models, in terms of BOLD variance explained, using resting-state fMRI data from the Human Connectome Project (N=100). We examined both one-input (heart rate or respiration) and two-input (heart rate and respiration) PRF models and show that allowing PRF curves to vary across subjects and brain regions generally improves PRF model performance. For one-input models, the improvement in model performance gained by allowing spatial variability was most prominent for respiration, particularly for a subset of the subjects (about a third) examined. Allowing for subject-specific or regional variability in the cardiac response function resulted in a significant model performance improvement only when using a two-input PRF model. Overall, our results highlight the importance of considering spatial and subject-specific variability in PRFs when analyzing fMRI data, particularly regarding respiratory-related fluctuations.

bioengineering↗