# 4D Attention-based Neural Network for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2101.05484 · HTML: https://mlsanity.com/paper/2101.05484/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 14 Jan 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Identifying Stable Patterns over Time for Emotion Recognition from EEG](https://mlsanity.com/paper/1601.02197/) (2016)
- [A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition](https://mlsanity.com/paper/1906.01704/) (2019)

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

- [ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data](https://mlsanity.com/paper/2308.00454/) (2023)
- [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://mlsanity.com/paper/2212.03176/) (2022)
- [Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition](https://mlsanity.com/paper/2110.09955/) (2021)
- [EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition](https://mlsanity.com/paper/2308.05767/) (2023)
- [Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces](https://mlsanity.com/paper/2502.17445/) (2025)
- [Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces](https://mlsanity.com/paper/2502.19281/) (2025)
- [CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition](https://mlsanity.com/paper/2305.05548/) (2023)
- [EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation](https://mlsanity.com/paper/2401.06957/) (2024)
- [The Role of Review Process Failures in Affective State Estimation: An Empirical Investigation of DEAP Dataset](https://mlsanity.com/paper/2508.02417/) (2025)
- [Local-Global Feature Fusion for Subject-Independent EEG Emotion Recognition](https://mlsanity.com/paper/2601.08094/) (2026)
- [Atoms of Thought: Universal EEG Representation Learning with Microstates](https://mlsanity.com/paper/2605.20182/) (2026)

## Abstract (arXiv)

Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the information contained in different domains of EEG signals. In this paper, we present a novel method, called four-dimensional attention-based neural network (4D-aNN) for EEG emotion recognition. First, raw EEG signals are transformed into 4D spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts spectral and spatial attention mechanisms to adaptively assign the weights of different brain regions and frequency bands, and a convolutional neural network (CNN) is utilized to deal with the spectral and spatial information of the 4D representations. Moreover, a temporal attention mechanism is integrated into a bidirectional Long Short-Term Memory (LSTM) to explore temporal dependencies of the 4D representations. Our model achieves state-of-the-art performance on the SEED dataset under intra-subject splitting. The experimental results have shown the effectiveness of the attention mechanisms in different domains for EEG emotion recognition.