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

arXiv: https://arxiv.org/abs/2309.07690 · HTML: https://mlsanity.com/paper/2309.07690/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses](https://mlsanity.com/paper/1602.05702/) (2016)
- [Riemannian geometry-based decoding of the directional focus of auditory attention using EEG](https://mlsanity.com/paper/2010.07171/) (2020)
- [Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network](https://mlsanity.com/paper/2103.02183/) (2021)

## Built on by (most cited first, 9 of 9)

- [ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG](https://mlsanity.com/paper/2401.04965/) (2024)
- [DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection](https://mlsanity.com/paper/2410.11181/) (2024)
- [SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding](https://mlsanity.com/paper/2409.19884/) (2024)
- [TAnet: A New Temporal Attention Network for EEG-based Auditory Spatial Attention Decoding with a Short Decision Window](https://mlsanity.com/paper/2401.05819/) (2024)
- [SHINE: Sequential Hierarchical Integration Network for EEG and MEG](https://mlsanity.com/paper/2602.23960/) (2026)
- [NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching](https://mlsanity.com/paper/2610.00397/) (2026)
- [Beyond Parallel Tracking: Interactive Multi-Feature Fusion Drives Semantic Reconstruction from Non-invasive Brain Recordings](https://mlsanity.com/paper/2607.12071/) (2026)
- [AFA-Net: A Differential Attention Approach for Auditory Attention Detection](https://mlsanity.com/paper/2609.31402/) (2026)
- [Merit: Making every modality earn its contribution in multimodal attention decoding](https://mlsanity.com/paper/2610.17507/) (2026)

## Abstract (arXiv)

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