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

arXiv: https://arxiv.org/abs/2410.11181 · HTML: https://mlsanity.com/paper/2410.11181/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [Improving Position Encoding of Transformers for Multivariate Time Series Classification](https://mlsanity.com/paper/2305.16642/) (2023)
- [An LSTM Based Architecture to Relate Speech Stimulus to EEG](https://mlsanity.com/paper/2002.10988/) (2020)
- [A DenseNet-based method for decoding auditory spatial attention with EEG](https://mlsanity.com/paper/2309.07690/) (2023)
- [Low-latency auditory spatial attention detection based on spectro-spatial features from EEG](https://mlsanity.com/paper/2103.03621/) (2021)
- [Riemannian geometry-based decoding of the directional focus of auditory attention using EEG](https://mlsanity.com/paper/2010.07171/) (2020)
- [DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection](https://mlsanity.com/paper/2309.07147/) (2023)

## Built on by (most cited first, 11 of 11)

- [S2M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection](https://mlsanity.com/paper/2508.05164/) (2025)
- [SHINE: Sequential Hierarchical Integration Network for EEG and MEG](https://mlsanity.com/paper/2602.23960/) (2026)
- [FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding](https://mlsanity.com/paper/2606.14120/) (2026)
- [Exploring Diffusion Transformers for Cross-Modal Augmentation in Multimodal Brain State Decoding](https://mlsanity.com/paper/2609.11341/) (2026)
- [NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching](https://mlsanity.com/paper/2610.00397/) (2026)
- [PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding](https://mlsanity.com/paper/2508.11357/) (2025)
- [Brainprint-Modulated Target Speaker Extraction](https://mlsanity.com/paper/2509.17883/) (2025)
- [Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions](https://mlsanity.com/paper/2603.12296/) (2026)
- [RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection](https://mlsanity.com/paper/2609.11372/) (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)

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.