BCI

In this preprint, we present a novel method to model the performance curve of EEG-based AAD algorithms starting from a single decision window length.

In this preprint, 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 article, accepted in IEEE Transactions on Biomedical Engineering, we present a new unsupervised method to estimate the performance of the stimulus reconstruction algorithm for EEG-based auditory attention decoding.

In this preprint, we present a wireless EEG sensor network of two independent miniature behind-the-ear sensor nodes.

In this report, we show that linear stimulus reconstruction (AAD) for EEG-based auditory attention decoding works on the KU Leuven audiovisual, gaze-controlled AAD dataset.

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

In this preprint, 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 paper, presented at EUSIPCO 2024, we present a probabilistic gain control system steered by brain-decoded auditory attention decoding algorithms.

In this article, we perform absolute attention decoding to speech based on EEG signals, to determine whether a subject is attending to the speech or not.

In this article, we demonstrate how a new object-based video feature leads to robust correlations with the decoded activity from EEG when subjects are watching natural videos.