7 papers · 1 filter
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…
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…