Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features

Ean Gyu Han, Tae Koo Kang, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This paper proposes a real-time emotion recognition system that utilizes photoplethysmography (PPG) and electromyography (EMG) physiological signals. The proposed approach employs a complex-valued neural network to extract common features from the physiological signals, enabling successful emotion recognition without interference. The system comprises three stages: single-pulse extraction, a physiological coherence feature module, and a physiological common feature module. The experimental results demonstrate that the proposed method surpasses alternative approaches in terms of accuracy and the recognition interval. By extracting common features of the PPG and EMG signals, this approach achieves effective emotion recognition without mutual interference. The findings provide a significant advancement in real-time emotion analysis and offer a clear and concise framework for understanding individuals’ emotional states using physiological signals.

Original languageEnglish
Article number2933
JournalElectronics (Switzerland)
Volume12
Issue number13
DOIs
Publication statusPublished - 2023 Jul

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (grant no. NRF-2022R1F1A1073543).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • complex-valued convolutional neural network (CVCNN)
  • convolutional autoencoder
  • EMG
  • emotion recognition
  • multimodal network
  • physiological signal
  • PPG
  • short-time Fourier transform (STFT)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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