5 papers
Post-processing of EEG-based Auditory Attention Decoding Decisions via Hidden Markov Models
Nicolas Heintz, Tom Francart, Alexander Bertrand
Auditory attention decoding (AAD) algorithms exploit brain signals, such as electroencephalography (EEG), to identify which speaker a listener is focusing on in a multi-speaker env…
Unsupervised EEG-based decoding of absolute auditory attention with canonical correlation analysis
Nicolas Heintz, Tom Francart, Alexander Bertrand
We propose a fully unsupervised algorithm that detects from encephalography (EEG) recordings when a subject actively listens to sound, versus when the sound is ignored. This proble…
Linear stimulus reconstruction works on the KU Leuven audiovisual, gaze-controlled auditory attention decoding dataset
Simon Geirnaert, Iustina Rotaru, Tom Francart +1
In a recent paper, we presented the KU Leuven audiovisual, gaze-controlled auditory attention decoding (AV-GC-AAD) dataset, in which we recorded electroencephalography (EEG) signal…
Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli
Simon Geirnaert, Yuanyuan Yao, Tom Francart +1
Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from…
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG
Bernd Accou, Hugo Van hamme, Tom Francart
Stimulus-evoked EEG data has a notoriously low signal-to-noise ratio and high inter-subject variability. We propose a novel paradigm for the self-supervised extraction of stimulus-…