1 citations · 1 across the 11 of their papers we have counts for
14 papers
A hidden Markov model for unsupervised EEG-based tracking of auditory attention to a single voice
Tianyi Li, Simon Geirnaert, Davina Van den Broek +3
Objective: Absolute auditory attention decoding (aAAD) aims to track how strongly a listener attends to a single speech source using electroencephalography (EEG). Unlike selective…
Multiscale Gaussian-Mixture Modeling for HMM Post-Processing in Selective Auditory Attention Decoding
Tianyi Li, Simon Geirnaert, Bert De Smedt +1
Selective auditory attention decoding (sAAD) infers from electroencephalography (EEG) which speaker a listener attends to in multi-speaker scenarios. A widely used approach reconst…
Why Performance Metrics Overpromise in Auditory Attention Decoding: an Information-Theoretic Reappraisal
Nicolas Heintz, Simon Geirnaert, Tom Francart +1
Auditory attention decoding (AAD) algorithms are predominantly evaluated in a steady state where a listener continuously attends to the same speaker, using metrics such as accuracy…
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…
Eccentricity Confound in EEG-based Visual Attention Decoding from Gaze-Fixated Neural Tracking of Motion in Natural Videos
Yuanyuan Yao, Celina Salamanca Gonzalez, Simon Geirnaert +3
Objective. Decoding visual attention from brain signals during naturalistic video viewing has emerged as a new direction in brain-computer interface research. Current methods assum…
Sample-level EEG-based Selective Auditory Attention Decoding with Markov Switching Models
Yuanyuan Yao, Simon Geirnaert, Tinne Tuytelaars +1
Selective auditory attention decoding aims to identify the speaker of interest from listeners' neural signals, such as electroencephalography (EEG), in the presence of multiple con…
Efficient Solutions for Mitigating Initialization Bias in Unsupervised Self-Adaptive Auditory Attention Decoding
Yuanyuan Yao, Simon Geirnaert, Tinne Tuytelaars +1
Decoding the attended speaker in a multi-speaker environment from electroencephalography (EEG) has attracted growing interest in recent years, with neuro-steered hearing devices as…
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…
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…
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…
EEG-based Decoding of Selective Visual Attention in Superimposed Videos
Yuanyuan Yao, Wout De Swaef, Simon Geirnaert +1
Selective attention enables humans to efficiently process visual stimuli by enhancing important elements and filtering out irrelevant information. Locating visual attention is fund…
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…