ArticleBioengineering (Basel, Switzerland)2022
A Survey on Physiological Signal-Based Emotion Recognition.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An Overview of Stress Analysis Based on Physiological Signals: Systematic Review of Open Datasets and Current Trends.Sensors (Basel, Switzerland) · 2025Pooled it
- A multimodal dataset for emotional transition analysis in virtual reality.Scientific data · 2026Article
- EEG and IMU Gait Signal Processing: A Comparative Assessment of the "Reza" Exponential Filter and Classical Filters.Sensors (Basel, Switzerland) · 2026Article
- Emotion recognition based on the temporal patterns of electroencephalogram signals and electrodermal response signals using the TRANSFORMER network.Frontiers in neuroscience · 2026Article
- Virtual reality-induced emotion recognition with deep learning-based multimodal physiological feature fusion.Frontiers in psychology · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- A comprehensive EEG dataset and performance assessment for Autism Spectrum Disorder.Scientific reports · 2025Article
- Segmentation-enhanced approach for emotion detection from EEG signals using the fuzzy C-mean and SVM.Scientific reports · 2025Article
- Cross-Subject Emotion Recognition with CT-ELCAN: Leveraging Cross-Modal Transformer and Enhanced Learning-Classify Adversarial Network.Bioengineering (Basel, Switzerland) · 2025Article
- Eye movements and user emotional experience: a study in interface design.Frontiers in psychology · 2025Article
- Heart rate variability responses to personalized and non-personalized affective videos. A study on healthy subjects and patients with disorders of consciousness.Frontiers in psychology · 2025Article
- Article
- Facial expression recognition (FER) survey: a vision, architectural elements, and future directions.PeerJ. Computer science · 2024Article
- I DARE: IULM Dataset of Affective Responses.Frontiers in human neuroscience · 2024Article
- Multi-Input Speech Emotion Recognition Model Using Mel Spectrogram and GeMAPS.Sensors (Basel, Switzerland) · 2023Article
- ECG-based Emotion Prediction in Real Life: Tackling Heterogeneity Through a Hybrid Personalization Method.IEEE transactions on affective computingArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 pre-processing, 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 pre-processing 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.
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