Improving auditory attention decoding performance of linear and non-linear methods using state-space model
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possible to identify the target speaker from single-trial EEG recordings using auditory attention decoding (AAD) methods. AAD methods reconstruct the attended speech envelope from EEG recordings, based on a linear least-squares cost function or non-linear neural networks, and then directly compare the reconstructed envelope with the speech envelopes of speakers to identify the attended speaker using Pearson correlation coefficients. Since these correlation coefficients are highly fluctuating, for a reliable decoding a large correlation window is used, which causes a large processing delay. In this paper, we investigate a state-space model using correlation coefficients obtained with a small correlation window to improve the decoding performance of the linear and the non-linear AAD methods. The experimental results show that the state-space model significantly improves the decoding performance.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGElectroencephalogram (EEG)Similar Papers 제목 키워드 기반
Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network
The auditory attention decoding (AAD) approach was proposed to determine the identity of the attended talker in a multi-talker scenario by analyzing electroencephalography (EEG) data. Although the linear model-based meth…
ClassificationEEGElectroencephalogram (EEG)General Classification+1Comparison of linear and nonlinear methods for decoding selective attention to speech from ear-EEG recordings
Many people with hearing loss struggle to comprehend speech in crowded auditory scenes, even when they are using hearing aids. It has recently been demonstrated that the focus of a listener's selective attention to speec…
EEGLinear stimulus reconstruction works on the KU Leuven audiovisual, gaze-controlled auditory attention decoding dataset
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) signals of participants attending to one out o…
EEGUnsupervised EEG-based decoding of absolute auditory attention with canonical correlation analysis
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 problem is known as absolute auditory attentio…
EEGA Direct Comparison of Simultaneously Recorded Scalp, Around-Ear, and In-Ear EEG for Neural Selective Auditory Attention Decoding to Speech
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 in complex acoustic environments like c…
EEG