# DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection

arXiv: https://arxiv.org/abs/2309.07147 · HTML: https://mlsanity.com/paper/2309.07147/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation.
- Builds on 14 of its 80 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Placed in

- Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/)

## Builds on (most cited first, 14 of 14)

- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices](https://mlsanity.com/paper/2008.04569/) (2020)
- [Learning From Yourself: A Self-Distillation Method for Fake Speech Detection](https://mlsanity.com/paper/2303.01211/) (2023)
- [Relating EEG to continuous speech using deep neural networks: a review](https://mlsanity.com/paper/2302.01736/) (2023)
- [Robust Spatial Filtering with Graph Convolutional Neural Networks](https://mlsanity.com/paper/1703.00792/) (2017)
- [EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses](https://mlsanity.com/paper/1602.05702/) (2016)
- [Extracting the Auditory Attention in a Dual-Speaker Scenario from EEG using a Joint CNN-LSTM Model](https://mlsanity.com/paper/2102.03957/) (2021)
- [Low-latency auditory spatial attention detection based on spectro-spatial features from EEG](https://mlsanity.com/paper/2103.03621/) (2021)
- [Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations](https://mlsanity.com/paper/2010.05404/) (2020)
- [Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model](https://mlsanity.com/paper/2106.09622/) (2021)

## Built on by (most cited first, 2 of 2)

- [DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection](https://mlsanity.com/paper/2410.11181/) (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)

## Abstract (arXiv)

Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0% and 79.6% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times.