The Psychological and Physiological Part of Emotions: Multimodal Approximation for Valence Classification
Jennifer Sorinas, Jose Manuel Ferr\'andez, Eduardo Fernandez

TL;DR
This study explores the psychobiology of central and peripheral nervous system signals for emotion recognition, using EEG, ECG, and skin temperature data from 24 subjects, and finds multimodal approaches do not outperform EEG alone.
Contribution
It provides a computational model for valence emotion recognition based on psychobiological signals and compares the effectiveness of multimodal versus single modality approaches.
Findings
EEG alone effectively classifies valence emotions.
Multimodal approach did not improve classification over EEG alone.
Sex differences influence physiological responses to emotions.
Abstract
In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their operation and the relationship between them remains unknown. In this context, in the present work we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence. To this end, the electroencephalography (EEG) signal, electrocardiography (ECG) signal and skin temperature of 24 subjects have been studied. Each methodology has been evaluated individually, finding characteristic patterns of positive and negative emotions in each of them. After feature selection of each methodology, the results…
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