# GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition

arXiv: https://arxiv.org/abs/2109.03124 · HTML: https://mlsanity.com/paper/2109.03124/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [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)
- [Spatial-Temporal Recurrent Neural Network for Emotion Recognition](https://mlsanity.com/paper/1705.04515/) (2017)
- [Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models](https://mlsanity.com/paper/2006.05331/) (2020)
- [TSception: A Deep Learning Framework for Emotion Detection Using EEG](https://mlsanity.com/paper/2004.02965/) (2020)
- [Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification](https://mlsanity.com/paper/1901.07429/) (2019)
- [Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information](https://mlsanity.com/paper/1809.04208/) (2018)
- [ScalingNet: extracting features from raw EEG data for emotion recognition](https://mlsanity.com/paper/2105.13987/) (2021)

## Built on by (most cited first, 17 of 17)

- [Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals](https://mlsanity.com/paper/2310.07648/) (2023)
- [Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2208.00877/) (2022)
- [Data Optimization in Deep Learning: A Survey](https://mlsanity.com/paper/2310.16499/) (2023)
- [EEG-based Multimodal Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2411.00822/) (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)
- [Robust Emotion Recognition via Bi-Level Self-Supervised Continual Learning](https://mlsanity.com/paper/2505.10575/) (2025)
- [Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper](https://mlsanity.com/paper/2306.03112/) (2023)
- [TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning](https://mlsanity.com/paper/2306.06522/) (2023)
- [Protecting Intellectual Property of EEG-based Neural Networks with Watermarking](https://mlsanity.com/paper/2502.05931/) (2025)
- [Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2507.14698/) (2025)
- [CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals](https://mlsanity.com/paper/2212.06413/) (2022)
- [Multi-Domain EEG Representation Learning with Orthogonal Mapping and Attention-based Fusion for Cognitive Load Classification](https://mlsanity.com/paper/2511.12394/) (2025)
- [Multimodal Functional Maximum Correlation for Emotion Recognition](https://mlsanity.com/paper/2512.23076/) (2025)
- [Three-Way Emotion Classification of EEG-based Signals using Machine Learning](https://mlsanity.com/paper/2602.00670/) (2026)
- [EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover](https://mlsanity.com/paper/2602.20041/) (2026)
- [Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving](https://mlsanity.com/paper/2604.19368/) (2026)

## Abstract (arXiv)

The data scarcity problem in Electroencephalography (EEG) based affective computing results into difficulty in building an effective model with high accuracy and stability using machine learning algorithms especially deep learning models. Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting. In this paper, we propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER). As the first to combine adversarial training with self-supervised learning for EEG-based emotion recognition, the proposed framework can generate high-quality and high-diversity simulated EEG samples. In particular, we utilize adversarial training to learn an EEG generator and force the generated EEG signals to approximate the distribution of real samples, ensuring the quality of augmented samples. A transformation function is employed to mask parts of EEG signals and force the generator to synthesize potential EEG signals based on the remaining parts, to produce a wide variety of samples. The masking possibility during transformation is introduced as prior knowledge to guide to extract distinguishable features for simulated EEG signals and generalize the classifier to the augmented sample space. Finally, extensive experiments demonstrate our proposed method can help emotion recognition for performance gain and achieve state-of-the-art results.