EEG-based detection of the locus of auditory attention with convolutional neural networks

被引：76

作者：

Vandecappelle, Servaas ^{[1
,2
]}

Deckers, Lucas ^{[1
,2
]}

Das, Neetha ^{[1
,2
]}

Ansari, Amir Hossein ^{[2
]}

Bertrand, Alexander ^{[2
]}

Francart, Tom ^{[1
]}

机构：

[1] Dept Neurosci, Expt Otorhinolaryngol, Leuven, Belgium

[2] Stadius Ctr Dynam Syst Signal Proc & Data Analyt, Dept Elect Engn ESAT, Leuven, Belgium

来源：

ELIFE | 2021年 / 10卷

基金：

欧洲研究理事会;

关键词：

SELECTIVE ATTENTION; TRACKING; SPEECH; MOTION; BRAIN;

D O I：

10.7554/eLife.56481

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1-2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.

引用

页数：17

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