2 citations · 5 across the 8 of their papers we have counts for
8 papers
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…
The role of vowel and consonant onsets in neural tracking of natural speech
Mohammad Jalilpour Monesi, Jonas Vanthornhout, Hugo Van hamme +1
To investigate how the auditory system processes natural speech, models have been created to relate the electroencephalography (EEG) signal of a person listening to speech to vario…
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…
Relating EEG to continuous speech using deep neural networks: a review
Corentin Puffay, Bernd Accou, Lies Bollens +4
Objective. When a person listens to continuous speech, a corresponding response is elicited in the brain and can be recorded using electroencephalography (EEG). Linear models are p…
Relating the fundamental frequency of speech with EEG using a dilated convolutional network
Corentin Puffay, Jana Van Canneyt, Jonas Vanthornhout +2
To investigate how speech is processed in the brain, we can model the relation between features of a natural speech signal and the corresponding recorded electroencephalogram (EEG)…