4 papers
Why Performance Metrics Overpromise in Auditory Attention Decoding: an Information-Theoretic Reappraisal
Nicolas Heintz, Simon Geirnaert, Tom Francart +1
Auditory attention decoding (AAD) algorithms are predominantly evaluated in a steady state where a listener continuously attends to the same speaker, using metrics such as accuracy…
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…
Minimally Informed Linear Discriminant Analysis: training an LDA model with unlabelled data
Nicolas Heintz, Tom Francart, Alexander Bertrand
Linear Discriminant Analysis (LDA) is one of the oldest and most popular linear methods for supervised classification problems. In this paper, we demonstrate that it is possible to…