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Physiological Signals are the most reliable form of signals for emotionrecognition, as they cannot be controlled deliberately by the subject. Existingreview papers on emotion recognition based on physiological signals surveyedonly the regular steps involved in the workflow of emotion recognition such aspreprocessing, feature extraction, and classification. While these areimportant steps, such steps are required for any signal processing application.Emotion recognition poses its own set of challenges that are very important toaddress for a robust system. Thus, to bridge the gap in the existingliterature, in this paper, we review the effect of inter-subject data varianceon emotion recognition, important data annotation techniques for emotionrecognition and their comparison, data preprocessing techniques for eachphysiological signal, data splitting techniques for improving thegeneralization of emotion recognition models and different multimodal fusiontechniques and their comparison. Finally we discuss key challenges and futuredirections in this field.

A Survey on Physiological Signal Based Emotion Recognition