AAD

In this preprint, we present efficient unsupervised training algorithms for EEG-based auditory attention decoding that have no initialization bias.

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, 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 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 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 try to uncover potential biases of the CSP-based decoding of the spatial focus of auditory attention, using a multi-talker audiovisual EEG experiment.

In this article, presented at ICASSP 2023, we present an update of the unsupervised stimulus decoder that avoids its inherent bias to the initialization.