# GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2205.01030 · HTML: https://mlsanity.com/paper/2205.01030/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 66 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 30 of 32)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory](https://mlsanity.com/paper/1609.02132/) (2016)
- [Graph Neural Networks with convolutional ARMA filters](https://mlsanity.com/paper/1901.01343/) (2019)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition](https://mlsanity.com/paper/2010.05599/) (2020)
- [Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](https://mlsanity.com/paper/1910.05552/) (2019)
- [A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition](https://mlsanity.com/paper/1906.01704/) (2019)
- [Spatial-Temporal Recurrent Neural Network for Emotion Recognition](https://mlsanity.com/paper/1705.04515/) (2017)
- [Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface](https://mlsanity.com/paper/1708.06578/) (2017)
- [Deep Constraint-based Propagation in Graph Neural Networks](https://mlsanity.com/paper/2005.02392/) (2020)

## Built on by (most cited first, 14 of 14)

- [Graph Neural Networks in EEG-based Emotion Recognition: A Survey](https://mlsanity.com/paper/2402.01138/) (2024)
- [A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation](https://mlsanity.com/paper/2310.03747/) (2023)
- [Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2405.19373/) (2024)
- [A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification](https://mlsanity.com/paper/2312.09623/) (2023)
- [Hybrid-supervised Hypergraph-enhanced Transformer for Micro-gesture Based Emotion Recognition](https://mlsanity.com/paper/2507.14867/) (2025)
- [EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition](https://mlsanity.com/paper/2308.05767/) (2023)
- [Semi-Supervised End-To-End Contrastive Learning For Time Series Classification](https://mlsanity.com/paper/2310.08848/) (2023)
- [Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation](https://mlsanity.com/paper/2408.10235/) (2024)
- [Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition](https://mlsanity.com/paper/2501.14246/) (2025)
- [FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition](https://mlsanity.com/paper/2503.18998/) (2025)
- [HEDN: A Hard-Easy Dual Network with Source Reliability Assessment for Cross-Subject EEG Emotion Recognition](https://mlsanity.com/paper/2511.06782/) (2025)
- [Task-guided Spatiotemporal Network with Diffusion Augmentation for EEG-based Dementia Diagnosis and MMSE Prediction](https://mlsanity.com/paper/2604.23964/) (2026)
- [Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning](https://mlsanity.com/paper/2605.22379/) (2026)
- [RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning](https://mlsanity.com/paper/2606.15278/) (2026)

## Abstract (arXiv)

Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a graph-based multi-task self-supervised learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks. By learning from multiple tasks simultaneously, GMSS can find a representation that captures all of the tasks thereby decreasing the chance of overfitting on the original task, i.e., emotion recognition task. In particular, the spatial jigsaw puzzle task aims to capture the intrinsic spatial relationships of different brain regions. Considering the importance of frequency information in EEG emotional signals, the goal of the frequency jigsaw puzzle task is to explore the crucial frequency bands for EEG emotion recognition. To further regularize the learned features and encourage the network to learn inherent representations, contrastive learning task is adopted in this work by mapping the transformed data into a common feature space. The performance of the proposed GMSS is compared with several popular unsupervised and supervised methods. Experiments on SEED, SEED-IV, and MPED datasets show that the proposed model has remarkable advantages in learning more discriminative and general features for EEG emotional signals.