# Spatial-temporal Transformers for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2110.06553 · HTML: https://mlsanity.com/paper/2110.06553/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2021.

## Built on by (most cited first, 5 of 5)

- [Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding](https://mlsanity.com/paper/2409.03251/) (2024)
- [Transformer-based EEG Decoding: A Survey](https://mlsanity.com/paper/2507.02320/) (2025)
- [Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression](https://mlsanity.com/paper/2404.15311/) (2024)
- [CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition](https://mlsanity.com/paper/2305.05548/) (2023)
- [LEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognition](https://mlsanity.com/paper/2504.09156/) (2025)

## Abstract (arXiv)

Electroencephalography (EEG) is a popular and effective tool for emotion recognition. However, the propagation mechanisms of EEG in the human brain and its intrinsic correlation with emotions are still obscure to researchers. This work proposes four variant transformer frameworks~(spatial attention, temporal attention, sequential spatial-temporal attention and simultaneous spatial-temporal attention) for EEG emotion recognition to explore the relationship between emotion and spatial-temporal EEG features. Specifically, spatial attention and temporal attention are to learn the topological structure information and time-varying EEG characteristics for emotion recognition respectively. Sequential spatial-temporal attention does the spatial attention within a one-second segment and temporal attention within one sample sequentially to explore the influence degree of emotional stimulation on EEG signals of diverse EEG electrodes in the same temporal segment. The simultaneous spatial-temporal attention, whose spatial and temporal attention are performed simultaneously, is used to model the relationship between different spatial features in different time segments. The experimental results demonstrate that simultaneous spatial-temporal attention leads to the best emotion recognition accuracy among the design choices, indicating modeling the correlation of spatial and temporal features of EEG signals is significant to emotion recognition.