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Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with

Lizheng Pan1, Ziqin Tang1, Shunchao Wang1

  • 1School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, People's Republic of China.

Physiological Measurement
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new hierarchical feature optimization method for emotion identification using physiological signals. The approach achieves high accuracy in cross-subject emotion recognition, outperforming existing techniques.

Keywords:
emotion recognitionfeature optimizationfeature selectionmulti-kernel function collaborationsupport vector machine

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Area of Science:

  • Affective computing
  • Human-computer interaction
  • Biomedical signal processing

Background:

  • Emotion recognition from physiological signals is challenging due to individual variability.
  • Accurate cross-subject emotion identification requires robust feature representation and classification.
  • Existing methods often struggle with the complexity and multi-channel nature of physiological data.

Purpose of the Study:

  • To develop a hierarchical feature optimization method for effective emotion representation using peripheral physiological signals.
  • To enhance the performance of emotion classification across different subjects.
  • To improve upon existing support vector machine (SVM) limitations in emotion recognition tasks.

Main Methods:

  • Proposed a hierarchical feature optimization method involving sparse learning and binary search for single signal feature selection.
  • Implemented an improved fast correlation-based filter for multi-channel signal feature fusion optimization.
  • Introduced a multi-kernel function collaboration strategy for support vector machine (SVM) classification.

Main Results:

  • Validated the proposed method on the DEAP dataset for cross-subject emotion identification.
  • Achieved competitive performance with accuracies of 84% (group 1) and 85.07% (group 2) for four types of emotion identification.
  • Demonstrated superior emotion recognition accuracy compared to state-of-the-art techniques.

Conclusions:

  • The proposed hierarchical feature optimization and multi-kernel SVM strategy significantly improves cross-subject emotion recognition accuracy.
  • The method offers a novel perspective for objective and comprehensive emotion recognition analysis.
  • This approach holds promise for advancing the field of affective computing and personalized human-computer interaction.