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Non-Parametric Classifiers Based Emotion Classification Using Electrodermal Activity and Modified Hjorth Features.

Yedukondala Rao Veeranki1, Nagarajan Ganapathy2, Ramakrishnan Swaminathan1

  • 1Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.

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Summary

This study classifies emotional states using Electrodermal Activity (EDA) signals with modified Hjorth features and non-parametric classifiers. The rotation forest classifier combined with these features achieved the highest accuracy in recognizing emotional dimensions.

Keywords:
Electrodermal activityEmotional statesModified Hjorth featuresNon-parametric classifiers

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Area of Science:

  • Psychophysiology
  • Biomedical Signal Processing
  • Machine Learning in Healthcare

Background:

  • Electrodermal Activity (EDA) is a sensitive indicator of autonomic nervous system arousal.
  • Classifying emotional states from physiological signals is crucial for mental health monitoring.
  • Existing methods may lack robustness in capturing the nuances of emotional responses.

Purpose of the Study:

  • To develop and evaluate a novel method for classifying emotional states using Electrodermal Activity (EDA) signals.
  • To investigate the efficacy of modified Hjorth features for emotional state discrimination.
  • To compare the performance of various non-parametric classifiers in this task.

Main Methods:

  • EDA signals were sourced from a public online database.
  • EDA signals were decomposed into Skin Conductance Level (SCL) and Skin Conductance Response (SCR).
  • Five modified Hjorth features (activity, mobility, complexity, chaos, hazard) were extracted from SCL and SCR.
  • Four non-parametric classifiers (random forest, k-nearest neighbor, support vector machine, rotation forest) were employed for classification.

Main Results:

  • The proposed approach successfully classified emotional states from EDA signals.
  • Most extracted features demonstrated statistical significance in differentiating emotional states.
  • The combination of modified Hjorth features and the rotation forest classifier yielded the highest classification accuracy.
  • The method showed effectiveness in recognizing valence and arousal dimensions.

Conclusions:

  • Modified Hjorth features combined with the rotation forest classifier provide an accurate method for emotional state classification from EDA.
  • This approach holds potential for applications in clinical settings for monitoring emotional well-being.
  • The findings contribute to the advancement of objective methods for emotion recognition using physiological data.