A DenseNet-based method for decoding auditory spatial attention with EEG

TL;DR

This paper introduces a DenseNet-based deep learning approach that transforms EEG data into a 3D spatial-temporal map to improve decoding accuracy of auditory spatial attention in EEG recordings.

Contribution

It proposes a novel 3D CNN method leveraging spatial EEG topologies, outperforming existing methods in auditory attention decoding accuracy.

Findings

Achieves 94.3% decoding accuracy with 1-second decision window.

Outperforms the state-of-the-art method XANet.

Utilizes 3D DenseNet to extract spatial-temporal features from EEG data.

Abstract

Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings. In the previous ASAD methods, the spatial distribution of EEG electrodes is not fully exploited, which may limit the performance of these methods. In the present work, by transforming the original EEG channels into a two-dimensional (2D) spatial topological map, the EEG data is transformed into a three-dimensional (3D) arrangement containing spatial-temporal information. And then a 3D deep convolutional neural network (DenseNet-3D) is used to extract temporal and spatial features of the neural representation for the attended locations. The results show that the proposed method achieves higher decoding accuracy than the state-of-the-art (SOTA) method (94.3% compared to XANet's 90.6%) with 1-second decision window for the widely used KULeuven (KUL) dataset, and the code to implement our work is available on Github: https://github.com/xuxiran/ASAD_DenseNet