A Survey on Physiological Signal-Based Emotion Recognition

• Published in: Bioengineering (Basel) Vol. 9; no. 11; p. 688
• Main Authors: Ahmad, Zeeshan, Khan, Naimul
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022; MDPI
• Subjects: Annotations; Bioengineering; challenges; Classification; Coronaviruses; COVID-19; data annotation; Data processing; data variability; Deep learning; Electrocardiography; Electroencephalography; emotion models; Emotion recognition; Emotions; Feature extraction; Literature reviews; Machine learning; Medical research; Methods; Pandemics; physiological signals; Physiology; review; Signal processing; Workflow; United States
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering9110688

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.