# EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices

arXiv: https://arxiv.org/abs/2008.04569 · HTML: https://mlsanity.com/paper/2008.04569/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 20 of 20)

- [BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction](https://mlsanity.com/paper/2312.14871/) (2023)
- [SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding](https://mlsanity.com/paper/2409.19884/) (2024)
- [BASEN: Time-Domain Brain-Assisted Speech Enhancement Network with Convolutional Cross Attention in Multi-talker Conditions](https://mlsanity.com/paper/2305.09994/) (2023)
- [AAD-LLM: Neural Attention-Driven Auditory Scene Understanding](https://mlsanity.com/paper/2502.16794/) (2025)
- [Predicting speech intelligibility from EEG in a non-linear classification paradigm](https://mlsanity.com/paper/2105.06844/) (2021)
- [DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection](https://mlsanity.com/paper/2309.07147/) (2023)
- [Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals](https://mlsanity.com/paper/2209.03990/) (2022)
- [A contrastive-learning approach for auditory attention detection](https://mlsanity.com/paper/2410.18395/) (2024)
- [Single-word Auditory Attention Decoding Using Deep Learning Model](https://mlsanity.com/paper/2410.19793/) (2024)
- [Multi-class Decoding of Attended Speaker Direction Using Electroencephalogram and Audio Spatial Spectrum](https://mlsanity.com/paper/2411.06928/) (2024)
- [S2M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection](https://mlsanity.com/paper/2508.05164/) (2025)
- [FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding](https://mlsanity.com/paper/2606.14120/) (2026)
- [NeuroSpex: Neuro-Guided Speaker Extraction with Cross-Modal Attention](https://mlsanity.com/paper/2409.02489/) (2024)
- [AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked Paradigm](https://mlsanity.com/paper/2501.03571/) (2025)
- [Scattering Transform for Auditory Attention Decoding](https://mlsanity.com/paper/2602.23003/) (2026)
- [Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training](https://mlsanity.com/paper/2606.24164/) (2026)
- [Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models](https://mlsanity.com/paper/2607.18345/) (2026)
- [RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection](https://mlsanity.com/paper/2609.11372/) (2026)
- [MAESTRO: a Multimodal Auditory-attention Egocentric Speech-TRacking Open corpus](https://mlsanity.com/paper/2609.31898/) (2026)
- [Merit: Making every modality earn its contribution in multimodal attention decoding](https://mlsanity.com/paper/2610.17507/) (2026)

## Abstract (arXiv)

People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information on which of these speakers the user actually aims to attend to. The correct (attended) speaker can then be enhanced using this information, and all other speakers can be treated as background noise. Recent neuroscientific advances have shown that it is possible to determine the focus of auditory attention from non-invasive neurorecording techniques, such as electroencephalography (EEG). Based on these new insights, a multitude of auditory attention decoding (AAD) algorithms have been proposed, which could, combined with the appropriate speaker separation algorithms and miniaturized EEG sensor devices, lead to so-called neuro-steered hearing devices. In this paper, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.