# A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2307.03068 · HTML: https://mlsanity.com/paper/2307.03068/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 68 references that are in the map.
- First posted on arXiv 6 Jul 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing](https://mlsanity.com/paper/1905.07039/) (2019)
- [Prediction of Reaction Time and Vigilance Variability from Spatiospectral Features of Resting-State EEG in a Long Sustained Attention Task](https://mlsanity.com/paper/1910.10076/) (2019)
- [Online Discriminative Graph Learning from Multi-Class Smooth Signals](https://mlsanity.com/paper/2101.00184/) (2021)

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

- [Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2402.09438/) (2024)
- [Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition](https://mlsanity.com/paper/2401.02344/) (2024)
- [Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model](https://mlsanity.com/paper/2404.15373/) (2024)
- [Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognition](https://mlsanity.com/paper/2610.13013/) (2026)

## Abstract (arXiv)

Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately, deep neural networks have shown promising performance in emotion recognition tasks. However, designing a deep architecture that can extract practical information from raw data is still a challenge. Here, we introduce a deep neural network that acquires interpretable physiological representations by a hybrid structure of spatio-temporal encoding and recurrent attention network blocks. Furthermore, a preprocessing step is applied to the raw data using graph signal processing tools to perform graph smoothing in the spatial domain. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset. To explore the generality of the learned model, we also evaluate the performance of our architecture towards transfer learning (TL) by transferring the model parameters from a specific source to other target domains. Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli.