AAD

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

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 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.

In this article, publlished in IEEE Journal of Biomedical and Health Informatics, we present a new time-adapative unsupervised stimulus decoder for AAD.