# A Survey on Physiological Signal Based Emotion Recognition

arXiv: https://arxiv.org/abs/2205.10466 · HTML: https://mlsanity.com/paper/2205.10466/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 120 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Survey on Emotional Body Gesture Recognition](https://mlsanity.com/paper/1801.07481/) (2018)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [Self-supervised Learning for ECG-based Emotion Recognition](https://mlsanity.com/paper/1910.07497/) (2019)
- [ECG Heartbeat Classification Using Multimodal Fusion](https://mlsanity.com/paper/2107.09869/) (2021)
- [An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis](https://mlsanity.com/paper/2203.15829/) (2022)
- [Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal](https://mlsanity.com/paper/2008.05503/) (2020)
- [ECG Heart-beat Classification Using Multimodal Image Fusion](https://mlsanity.com/paper/2105.13536/) (2021)

## Built on by (most cited first, 12 of 12)

- [A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study](https://mlsanity.com/paper/2308.14245/) (2023)
- [Perception for Humanoid Robots](https://mlsanity.com/paper/2309.15616/) (2023)
- [Empathetic Conversational Agents: Utilizing Neural and Physiological Signals for Enhanced Empathetic Interactions](https://mlsanity.com/paper/2501.08393/) (2025)
- [Enhancing Generalization in PPG-Based Emotion Measurement with a CNN-TCN-LSTM Model](https://mlsanity.com/paper/2507.14173/) (2025)
- [Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals](https://mlsanity.com/paper/2305.00769/) (2023)
- [Stress Classification from ECG Signals Using Vision Transformer](https://mlsanity.com/paper/2603.26721/) (2026)
- [Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being](https://mlsanity.com/paper/2502.17172/) (2025)
- [Introducing WARM-VR: Benchmark Dataset for Multimodal Wearable Affect Recognition in Virtual Reality](https://mlsanity.com/paper/2605.00184/) (2026)
- [BioKD: Selective Physiology-to-Video Knowledge Distillation via Reliability Gate for Emotion Recognition](https://mlsanity.com/paper/2608.06023/) (2026)
- [Pseudo-Label Augmentation for Affect Sensing in Small Collaborative Groups](https://mlsanity.com/paper/2609.16077/) (2026)
- [From Stress to Affect: Multimodal Deep Learning for Physiological Emotion Recognition Across Wearable Sensor Modalities](https://mlsanity.com/paper/2609.20991/) (2026)
- [Detecting Agitation Before Behavioral Escalation in Autistic Youth Through Multimodal Wearable Sensing](https://mlsanity.com/paper/2609.24791/) (2026)

## Abstract (arXiv)

Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data preprocessing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally we discuss key challenges and future directions in this field.