Classification of emotions based on heart rate variability

• Grant number: 21/05658-0
• Support Opportunities: Scholarships in Brazil - Master
• Start date: July 01, 2021
• End date: June 30, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: André Fujita
• Grantee: Mario Muramatsu Junior
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 20/01479-1 - Classification of body/mental states for a human-machine interface based on the heart rate variability, AP.ESCIENCE.R

Abstract

Evidence suggests that emotions/brain states involve patterns of behavior, physiological, and neural activation. Some emotions have been successfully classified from peripheral physiological responses, bodily activation patterns (Kragel and Labar, 2015; Kreibig et al., 2007; Nummenmaa et al., 2014; Hietanen et al. 2016; Siegel et al., 2018), and subjective feelings (Saarimäki et al., 2018). These results suggest that different emotions have a distinguishable characteristic in expressions, bodily sensations, and subjective experiences (Nummenmaa and Saarimäki, 2019). At the neural level, studies have revealed that the cortical midline and somatomotor regions and limbic subcortical regions underlie different emotion categories (Chikazoe et al., 2014; Kragel and LaBar, 2015; Peelen et al., 2010; Saarimäki et al., 2018). The cortical midline, somatosensory regions, and limbic subcortical areas all have solid reciprocal connections to heart rate control areas. Hence, we will test whether the heart rate variability (HRV) alone can distinguish some emotions. This project is associated with "Goal 4" of the primary document. The master student will be supervised by Prof. Fujita (Computer Science) and co-supervised by Prof. Takahashi (Life Science). (AU)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

MURAMATSU JUNIOR, Mario. Heart rate variability predicts low cognitive capacity choices. 2024. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI) São Paulo.