# EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2406.18345 · HTML: https://mlsanity.com/paper/2406.18345/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [MetaFormer Is Actually What You Need for Vision](https://mlsanity.com/paper/2111.11418/) (2021)
- [CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters](https://mlsanity.com/paper/1705.07664/) (2017)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2109.09559/) (2021)
- [TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition](https://mlsanity.com/paper/2104.02935/) (2021)
- [AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition](https://mlsanity.com/paper/2212.12134/) (2022)

## Built on by (most cited first, 24 of 24)

- [EEG-based Multimodal Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2411.00822/) (2024)
- [MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition](https://mlsanity.com/paper/2507.15914/) (2025)
- [SingLEM: Single-Channel Large EEG Model](https://mlsanity.com/paper/2509.17920/) (2025)
- [DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis](https://mlsanity.com/paper/2407.20519/) (2024)
- [PL-DCP: A Pairwise Learning framework with Domain and Class Prototypes for EEG emotion recognition under unseen target conditions](https://mlsanity.com/paper/2412.00082/) (2024)
- [Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer](https://mlsanity.com/paper/2504.03762/) (2025)
- [FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition](https://mlsanity.com/paper/2506.22807/) (2025)
- [HEEGNet: Hyperbolic Embeddings for EEG](https://mlsanity.com/paper/2601.03322/) (2026)
- [LEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognition](https://mlsanity.com/paper/2504.09156/) (2025)
- [Functional embeddings enable Aggregation of multi-area SEEG recordings over subjects and sessions](https://mlsanity.com/paper/2510.27090/) (2025)
- [Learning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition](https://mlsanity.com/paper/2509.01135/) (2025)
- [DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm](https://mlsanity.com/paper/2509.07991/) (2025)
- [Region-aware Spatiotemporal Modeling with Collaborative Domain Generalization for Cross-Subject EEG Emotion Recognition](https://mlsanity.com/paper/2601.15615/) (2026)
- [DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models](https://mlsanity.com/paper/2604.15016/) (2026)
- [Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding](https://mlsanity.com/paper/2605.00857/) (2026)
- [GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition](https://mlsanity.com/paper/2605.25061/) (2026)
- [Context-aware tokenization for Cross-subject Emotion Decoding from EEG](https://mlsanity.com/paper/2606.00884/) (2026)
- [RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning](https://mlsanity.com/paper/2606.15278/) (2026)
- [PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition](https://mlsanity.com/paper/2607.00358/) (2026)
- [Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition](https://mlsanity.com/paper/2607.04139/) (2026)
- [EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding](https://mlsanity.com/paper/2608.13072/) (2026)
- [Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures](https://mlsanity.com/paper/2609.22103/) (2026)
- [Adaptive Forgetting for Nonstationary Optimization: Towards Robust EEG Decoding](https://mlsanity.com/paper/2609.24233/) (2026)
- [Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS](https://mlsanity.com/paper/2610.02796/) (2026)

## Abstract (arXiv)

Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial and short-term temporal patterns, there has been limited emphasis on capturing the vital long-term contextual information associated with emotional cognitive processes. In order to address this discrepancy, we introduce a novel transformer model called emotion transformer (EmT). EmT is designed to excel in both generalized cross-subject EEG emotion classification and regression tasks. In EmT, EEG signals are transformed into a temporal graph format, creating a sequence of EEG feature graphs using a temporal graph construction module (TGC). A novel residual multi-view pyramid GCN module (RMPG) is then proposed to learn dynamic graph representations for each EEG feature graph within the series, and the learned representations of each graph are fused into one token. Furthermore, we design a temporal contextual transformer module (TCT) with two types of token mixers to learn the temporal contextual information. Finally, the task-specific output module (TSO) generates the desired outputs. Experiments on four publicly available datasets show that EmT achieves higher results than the baseline methods for both EEG emotion classification and regression tasks. The code is available at https://github.com/yi-ding-cs/EmT.