# Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network

arXiv: https://arxiv.org/abs/2103.02183 · HTML: https://mlsanity.com/paper/2103.02183/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [An LSTM Based Architecture to Relate Speech Stimulus to EEG](https://mlsanity.com/paper/2002.10988/) (2020)

## Built on by (most cited first, 4 of 4)

- [A DenseNet-based method for decoding auditory spatial attention with EEG](https://mlsanity.com/paper/2309.07690/) (2023)
- [Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection](https://mlsanity.com/paper/2307.08501/) (2023)
- [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)

## Abstract (arXiv)

The auditory attention decoding (AAD) approach was proposed to determine the identity of the attended talker in a multi-talker scenario by analyzing electroencephalography (EEG) data. Although the linear model-based method has been widely used in AAD, the linear assumption was considered oversimplified and the decoding accuracy remained lower for shorter decoding windows. Recently, nonlinear models based on deep neural networks (DNN) have been proposed to solve this problem. However, these models did not fully utilize both the spatial and temporal features of EEG, and the interpretability of DNN models was rarely investigated. In this paper, we proposed novel convolutional recurrent neural network (CRNN) based regression model and classification model, and compared them with both the linear model and the state-of-the-art DNN models. Results showed that, our proposed CRNN-based classification model outperformed others for shorter decoding windows (around 90% for 2 s and 5 s). Although worse than classification models, the decoding accuracy of the proposed CRNN-based regression model was about 5% greater than other regression models. The interpretability of DNN models was also investigated by visualizing layers' weight.