157 citations · 219 across the 18 of their papers we have counts for
19 papers
Post-processing of EEG-based Auditory Attention Decoding Decisions via Hidden Markov Models
Nicolas Heintz, Tom Francart, Alexander Bertrand
Auditory attention decoding (AAD) algorithms exploit brain signals, such as electroencephalography (EEG), to identify which speaker a listener is focusing on in a multi-speaker env…
Unsupervised EEG-based decoding of absolute auditory attention with canonical correlation analysis
Nicolas Heintz, Tom Francart, Alexander Bertrand
We propose a fully unsupervised algorithm that detects from encephalography (EEG) recordings when a subject actively listens to sound, versus when the sound is ignored. This proble…
Linear stimulus reconstruction works on the KU Leuven audiovisual, gaze-controlled auditory attention decoding dataset
Simon Geirnaert, Iustina Rotaru, Tom Francart +1
In a recent paper, we presented the KU Leuven audiovisual, gaze-controlled auditory attention decoding (AV-GC-AAD) dataset, in which we recorded electroencephalography (EEG) signal…
Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli
Simon Geirnaert, Yuanyuan Yao, Tom Francart +1
Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from…
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…
Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses
Simon Geirnaert, Tom Francart, Alexander Bertrand
In brain-computer interface or neuroscience applications, generalized canonical correlation analysis (GCCA) is often used to extract correlated signal components in the neural acti…
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…
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model
Mohammad Jalilpour Monesi, Bernd Accou, Tom Francart +1
Decoding the speech signal that a person is listening to from the human brain via electroencephalography (EEG) can help us understand how our auditory system works. Linear models h…
Predicting speech intelligibility from EEG in a non-linear classification paradigm
Bernd Accou, Mohammad Jalilpour Monesi, Hugo Van hamme +1
Objective: Currently, only behavioral speech understanding tests are available, which require active participation of the person being tested. As this is infeasible for certain pop…
Riemannian geometry-based decoding of the directional focus of auditory attention using EEG
Simon Geirnaert, Tom Francart, Alexander Bertrand
Auditory attention decoding (AAD) algorithms decode the auditory attention from electroencephalography (EEG) signals that capture the listener's neural activity. Such AAD methods a…
EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices
Simon Geirnaert, Servaas Vandecappelle, Emina Alickovic +6
People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously.…
An LSTM Based Architecture to Relate Speech Stimulus to EEG
Mohammad Jalilpour Monesi, Bernd Accou, Jair Montoya-Martinez +2
Modeling the relationship between natural speech and a recorded electroencephalogram (EEG) helps us understand how the brain processes speech and has various applications in neuros…
Head shadow enhancement with low-frequency beamforming improves sound localization and speech perception for simulated bimodal listeners
Benjamin Dieudonné, Tom Francart
Many hearing-impaired listeners struggle to localize sounds due to poor availability of binaural cues. Listeners with a cochlear implant and a contralateral hearing aid -- so-calle…
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses
Simon Van Eyndhoven, Tom Francart, Alexander Bertrand
OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy, two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which a…