2 citations · 2 across the 5 of their papers we have counts for
5 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…
Modeling and Interpreting Correlations, Null Distributions and Significance Levels in Neural Tracking of Natural Stimuli
Simon Geirnaert, Alexander Bertrand, Tom Francart +1
Neural tracking - the time-locking of neural responses to continuous stimuli such as speech, music, and video - is widely used to study how the brain processes natural input. Track…
A Multi-decoder Neural Tracking Method for Accurately Predicting Speech Intelligibility
Rien Sonck, Bernd Accou, Tom Francart +1
Objective: EEG-based methods can predict speech intelligibility, but their accuracy and robustness lag behind behavioral tests, which typically show test-retest differences under 1…
Performance Modeling for Correlation-based Neural Decoding of Auditory Attention to Speech
Simon Geirnaert, Jonas Vanthornhout, Tom Francart +1
Correlation-based auditory attention decoding (AAD) algorithms exploit neural tracking mechanisms to determine listener attention among competing speech sources via, e.g., electroe…
Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework
Pieter De Clercq, Corentin Puffay, Jill Kries +4
Aphasia, a language disorder primarily caused by a stroke, is traditionally diagnosed using behavioral language tests. However, these tests are time-consuming, require manual inter…