EEG

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 analysis of an educationally relevant paradigm to track attention to the teacher from EEG.

In this article, we present analysis of a conversation tracking paradigm with 2 or 3 conversations during selective auditory attention decoding from EEG.

In this article, we present a novel dataset and accompanying analysis enabling the first direct comparison between scalp, around-ear, and in-ear electroencephalography (EEG) for neural selective auditory attention decoding to speech.

In this article, we present a new end-to-end deep learning model, AADnet, for auditory attention decoding from EEG in multi-talker scenarios.

In this article, we present a new experiment in which participants attend on of two superimposed video, and we show that we can decode their attention from EEG and gaze data.

In this article, we present a new unsupervised method to estimate the performance of the stimulus reconstruction algorithm for EEG-based auditory attention decoding.