157 citations · 219 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG
Bernd Accou, Hugo Van hamme, Tom Francart
Stimulus-evoked EEG data has a notoriously low signal-to-noise ratio and high inter-subject variability. We propose a novel paradigm for the self-supervised extraction of stimulus-…
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…