8 citations · 10 across the 14 of their papers we have counts for
17 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…
A Direct Comparison of Simultaneously Recorded Scalp, Around-Ear, and In-Ear EEG for Neural Selective Auditory Attention Decoding to Speech
Simon Geirnaert, Simon L. Kappel, Preben Kidmose
Current assistive hearing devices, such as hearing aids and cochlear implants, lack the ability to adapt to the listener's focus of auditory attention, limiting their effectiveness…
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…
AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding
Nhan Duc Thanh Nguyen, Huy Phan, Simon Geirnaert +2
Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recorded through electroencephalogr…
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…
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…
Grouped Variable Selection for Generalized Eigenvalue Problems
Jonathan Dan, Simon Geirnaert, Alexander Bertrand
Many problems require the selection of a subset of variables from a full set of optimization variables. The computational complexity of an exhaustive search over all possible subse…
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.…