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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 article, based on a new experiment, we analye how visual eccentricity affects neural responses to naturalistic videos when eye movements are controlled for.

In this preprint, we present an alternative post-processing method to hidden Markov models for auditory attention decoding algorithms, based on the Markov switching model.

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