DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection

TL;DR

DARNet is a novel neural network that improves auditory attention detection by capturing spatial and long-range temporal features in EEG signals, achieving higher accuracy with fewer parameters.

Contribution

This paper introduces DARNet, a dual attention refinement network with spatiotemporal construction, enhancing EEG feature representation and long-range dependency modeling for AAD.

Findings

DARNet improves classification accuracy by up to 5.9% over state-of-the-art.

DARNet reduces model parameters by 91%.

DARNet effectively captures spatial and temporal EEG features.

Abstract

At a cocktail party, humans exhibit an impressive ability to direct their attention. The auditory attention detection (AAD) approach seeks to identify the attended speaker by analyzing brain signals, such as EEG signals. However, current AAD algorithms overlook the spatial distribution information within EEG signals and lack the ability to capture long-range latent dependencies, limiting the model's ability to decode brain activity. To address these issues, this paper proposes a dual attention refinement network with spatiotemporal construction for AAD, named DARNet, which consists of the spatiotemporal construction module, dual attention refinement module, and feature fusion \& classifier module. Specifically, the spatiotemporal construction module aims to construct more expressive spatiotemporal feature representations, by capturing the spatial distribution characteristics of EEG signals. The dual attention refinement module aims to extract different levels of temporal patterns in EEG signals and enhance the model's ability to capture long-range latent dependencies. The feature fusion \& classifier module aims to aggregate temporal patterns and dependencies from different levels and obtain the final classification results. The experimental results indicate that compared to the state-of-the-art models, DARNet achieves an average classification accuracy improvement of 5.9\% for 0.1s, 4.6\% for 1s, and 3.9\% for 2s on the DTU dataset. While maintaining excellent classification performance, DARNet significantly reduces the number of required parameters. Compared to the state-of-the-art models, DARNet reduces the parameter count by 91\%. Code is available at: https://github.com/fchest/DARNet.git.