2 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Detecting post-stroke aphasia using EEG-based neural envelope tracking of natural speech
Pieter De Clercq, Jill Kries, Ramtin Mehraram +3
[Objective]. After a stroke, one-third of patients suffer from aphasia, a language disorder that impairs communication ability. The standard behavioral tests used to diagnose aphas…