21 citations · 25 across the 6 of their papers we have counts for
6 papers
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…
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…
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 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)…
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders
Lies Bollens, Tom Francart, Hugo Van Hamme
The electroencephalogram (EEG) is a powerful method to understand how the brain processes speech. Linear models have recently been replaced for this purpose with deep neural networ…