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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Related Experiment Video

Updated: May 29, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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Develop an emotion recognition system using jointly connectivity between electroencephalogram and electrocardiogram

Javid Farhadi Sedehi1, Nader Jafarnia Dabanloo1, Keivan Maghooli1

  • 1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Heliyon
|February 3, 2025
PubMed
Summary

This study enhances emotion recognition by combining electroencephalogram (EEG) and electrocardiogram (ECG) data. Novel effective connectivity methods significantly improve accuracy, demonstrating the power of multimodal physiological signals.

Keywords:
Convolutional neural network (CNN)Coupling EEG-ECGEffective connectivityEmotion recognitionTransfer learning

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Area of Science:

  • Physiological computing
  • Affective computing
  • Biomedical signal processing

Background:

  • Emotion recognition (ER) systems often rely on single-modality data, limiting accuracy.
  • Understanding the complex interplay between brain and heart activity is crucial for robust ER.

Purpose of the Study:

  • To develop and evaluate an innovative approach for improving ER accuracy by integrating electroencephalogram (EEG) and electrocardiogram (ECG) data.
  • To propose a novel method for estimating effective connectivity (EC) to capture cardio-cerebral dynamics during emotional states.

Main Methods:

  • Utilized three EC estimation techniques: Granger causality (GC), partial directed coherence (PDC), and directed transfer function (DTF).
  • Employed convolutional neural networks (CNNs), specifically ResNet-18 and MobileNetV2, to process EC representations.
  • Evaluated the approach using EEG and ECG data from the public MAHNOB-HCI database with 5-fold cross-validation.

Main Results:

  • Achieved high average accuracy, reaching 97.34% ± 1.19% with DTF images in the alpha frequency band using ResNet-18.
  • MobileNetV2 also demonstrated strong performance with 96.53% ± 3.54% accuracy using DTF images.
  • Comparative analyses confirmed substantial improvements over existing ER methods.

Conclusions:

  • Integrating EEG and ECG data via novel EC estimation significantly enhances emotion recognition performance.
  • The proposed multimodal approach offers a more accurate and dependable solution for ER systems.
  • This study highlights the efficacy of leveraging cardio-cerebral interactions for advanced affective computing.