# Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2110.09955 · HTML: https://mlsanity.com/paper/2110.09955/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [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)
- [4D Attention-based Neural Network for EEG Emotion Recognition](https://mlsanity.com/paper/2101.05484/) (2021)
- [Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition](https://mlsanity.com/paper/2004.01973/) (2020)

## Built on by (most cited first, 3 of 3)

- [You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation](https://mlsanity.com/paper/2406.15269/) (2024)
- [Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion Recognition](https://mlsanity.com/paper/2406.14014/) (2024)
- [CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition](https://mlsanity.com/paper/2305.05548/) (2023)

## Abstract (arXiv)

Recognizing the feelings of human beings plays a critical role in our daily communication. Neuroscience has demonstrated that different emotion states present different degrees of activation in different brain regions, EEG frequency bands and temporal stamps. In this paper, we propose a novel structure to explore the informative EEG features for emotion recognition. The proposed module, denoted by PST-Attention, consists of Positional, Spectral and Temporal Attention modules to explore more discriminative EEG features. Specifically, the Positional Attention module is to capture the activate regions stimulated by different emotions in the spatial dimension. The Spectral and Temporal Attention modules assign the weights of different frequency bands and temporal slices respectively. Our method is adaptive as well as efficient which can be fit into 3D Convolutional Neural Networks (3D-CNN) as a plug-in module. We conduct experiments on two real-world datasets. 3D-CNN combined with our module achieves promising results and demonstrate that the PST-Attention is able to capture stable patterns for emotion recognition from EEG.