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In this preprint, we propose a multiscale Gaussian mixture model that jointly estimates HMM emission parameters across multiple window lengths, improving HMM-based post-processing for selective auditory attention decoding.

In this preprint, we show that ignoring temporal dependence can substantially overestimate the performance of auditory attention decoding algorithms. We propose several measures to address these issues, including rIMI, a metric that quantifies the rate of new information gained.

In this preprint, we present a comprehensive framework for modeling and interpreting correlations, null distributions, and significance levels that are routinely use in neural tracking of natural stimuli.

In this preprint, we present a proof of concept of a wireless EEG sensor network using two around-ear EEG nodes for selective auditory attention decoding.

In this preprint, we show that real-time auditory neurofeedback in a closed-loop selective auditory attention decoding paradigm can degrade decoding accuracy and user experience, rather than improve it, within a single session.

In this preprint, we present analysis of an educationally relevant paradigm to track attention to the teacher from EEG.