Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning

TL;DR

This paper introduces a novel multi-view VAE with task-related contrastive learning for decoding auditory attention from EEG data, effectively leveraging missing view information to improve representation quality.

Contribution

It proposes a new multi-view VAE framework with TMC learning that incorporates prior knowledge and handles missing views for better auditory attention decoding.

Findings

Outperforms state-of-the-art methods on two AAD datasets.

Effectively utilizes missing view information to enhance decoding accuracy.

Demonstrates robustness across different datasets.

Abstract

The human brain can easily focus on one speaker and suppress others in scenarios such as a cocktail party. Recently, researchers found that auditory attention can be decoded from the electroencephalogram (EEG) data. However, most existing deep learning methods are difficult to use prior knowledge of different views (that is attended speech and EEG are task-related views) and extract an unsatisfactory representation. Inspired by Broadbent's filter model, we decode auditory attention in a multi-view paradigm and extract the most relevant and important information utilizing the missing view. Specifically, we propose an auditory attention decoding (AAD) method based on multi-view VAE with task-related multi-view contrastive (TMC) learning. Employing TMC learning in multi-view VAE can utilize the missing view to accumulate prior knowledge of different views into the fusion of representation, and extract the approximate task-related representation. We examine our method on two popular AAD datasets, and demonstrate the superiority of our method by comparing it to the state-of-the-art method.