14 citations · 16 across the 5 of their papers we have counts for
6 papers
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…
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…
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…
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…