BCI

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 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 article, to be presented at ICASSP 2026, we present efficient unsupervised training algorithms for EEG-based auditory attention decoding that have no initialization bias.

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, presented at EUSIPCO 2025, we present a novel method to model the performance curve of EEG-based AAD algorithms starting from a single decision window length.

In this paper, presented at EMBC 2025, we show how acoustic dynamic range of a speech stimulus influences the speech-EEG correlation when using that correlation as a metric for absolute attention.

In this paper, presented at EMBC 2025, we present a wireless EEG sensor network of two independent miniature behind-the-ear sensor nodes.