EEG

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 preprint, 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 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 preprint, 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 unsupervised method to estimate the performance of the stimulus reconstruction algorithm for EEG-based auditory attention decoding.

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 article, we present a new signal processing technique for the group analysis of stimulus-following neural responses, building upon our conference precursor. Again, all code is available!

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.