# EEG based Emotion Recognition: A Tutorial and Review

arXiv: https://arxiv.org/abs/2203.11279 · HTML: https://mlsanity.com/paper/2203.11279/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 246 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [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)
- [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)
- [Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models](https://mlsanity.com/paper/2006.05331/) (2020)
- [Multi-scale brain networks](https://mlsanity.com/paper/1608.08828/) (2016)
- [TSception: A Deep Learning Framework for Emotion Detection Using EEG](https://mlsanity.com/paper/2004.02965/) (2020)
- [Robust Spatial Filtering with Graph Convolutional Neural Networks](https://mlsanity.com/paper/1703.00792/) (2017)
- [Distilling EEG Representations via Capsules for Affective Computing](https://mlsanity.com/paper/2105.00104/) (2021)
- [Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification](https://mlsanity.com/paper/2003.06113/) (2020)
- [Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues](https://mlsanity.com/paper/1708.08729/) (2017)

## Built on by (most cited first, 30 of 43)

- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior](https://mlsanity.com/paper/2503.04207/) (2025)
- [A Survey on Physiological Signal Based Emotion Recognition](https://mlsanity.com/paper/2205.10466/) (2022)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives](https://mlsanity.com/paper/2504.20069/) (2025)
- [EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model](https://mlsanity.com/paper/2405.00574/) (2024)
- [Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers](https://mlsanity.com/paper/2307.02819/) (2023)
- [Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition](https://mlsanity.com/paper/2404.09559/) (2024)
- [GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network](https://mlsanity.com/paper/2309.15515/) (2023)
- [BioMamba: Leveraging Spectro-Temporal Embedding in Bidirectional Mamba for Enhanced Biosignal Classification](https://mlsanity.com/paper/2503.11741/) (2025)
- [BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding](https://mlsanity.com/paper/2606.25400/) (2026)
- [A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition](https://mlsanity.com/paper/2405.07260/) (2024)
- [Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion Recognition](https://mlsanity.com/paper/2406.14014/) (2024)
- [Electroencephalogram Emotion Recognition via AUC Maximization](https://mlsanity.com/paper/2408.08979/) (2024)
- [Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation](https://mlsanity.com/paper/2408.10235/) (2024)
- [A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection](https://mlsanity.com/paper/2410.18103/) (2024)
- [Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation](https://mlsanity.com/paper/2504.03707/) (2025)
- [Emotion Recognition from Skeleton Data: A Comprehensive Survey](https://mlsanity.com/paper/2507.18026/) (2025)
- [LEAF: Language-EEG Aligned Foundation Model for Brain-Computer Interfaces](https://mlsanity.com/paper/2509.24302/) (2025)
- [NeuroLex: A Lightweight Domain Language Model for EEG Report Understanding and Generation](https://mlsanity.com/paper/2511.12851/) (2025)
- [CognitionCapturerPro: Towards High-Fidelity Visual Decoding from EEG/MEG via Multi-modal Information and Asymmetric Alignment](https://mlsanity.com/paper/2603.12722/) (2026)
- [CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition](https://mlsanity.com/paper/2305.05548/) (2023)
- [Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper](https://mlsanity.com/paper/2306.03112/) (2023)
- [Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2405.16701/) (2024)
- [LEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognition](https://mlsanity.com/paper/2504.09156/) (2025)
- [Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2507.14698/) (2025)
- [EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking Neural Networks](https://mlsanity.com/paper/2304.07655/) (2023)
- [Thelxino"e: Recognizing Human Emotions Using Pupillometry and Machine Learning](https://mlsanity.com/paper/2403.19014/) (2024)
- [Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks](https://mlsanity.com/paper/2408.10328/) (2024)
- [Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review](https://mlsanity.com/paper/2409.07493/) (2024)

## Abstract (arXiv)

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions.