EEG-Based Auditory Attention Detection via Frequency and Channel Neural Attention

Abstract

Humans have the ability to pay attention to one of the sound sources in a multispeaker acoustic environment. Auditory attention detection (AAD) seeks to detect the attended speaker from one's brain signals that will enable many innovative human-machine systems. However, effective representation learning of electroencephalography (EEG) signals remains a challenge. In this article, we propose a neural attention mechanism that dynamically assigns differentiated weights to the subbands and the channels of EEG signals to derive discriminative representations for AAD. In the nutshell, we would like to build a computational attention mechanism, i.e., neural attention, to model the auditory attention in human brain. We incorporate the proposed neural attention into an AAD system, and validate the neural attention mechanism through comprehensive experiments on two publicly available datasets. The experimental results demonstrate that the proposed system significantly outperforms the state-of-the-art reference baselines.