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EEG rhythm based emotion recognition using multivariate decomposition and ensemble machine learning classifier.

Raveendrababu Vempati1, Lakhan Dev Sharma1

  • 1School of Electronics Engineering VIT-AP University, Andhra Pradesh, 522237, India.

Journal of Neuroscience Methods
|May 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for automatic emotion recognition using electroencephalogram (EEG) signals. Ensemble machine learning classifiers achieved high accuracy (93.5%-99.8%) in classifying emotions from EEG rhythmic features, particularly gamma rhythms.

Keywords:
EEG signalEmotion recognitionLeave-one-subject-out cross-validation (LOSOCV)Multivariate fast iterative filtering (mvFIF)Subspace KNN

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Area of Science:

  • Cognitive Neuroscience
  • Affective Computing
  • Human-Computer Interaction (HCI)

Background:

  • Electroencephalogram (EEG) signals show promise for recognizing human emotions.
  • Effective computing aims to enhance human-computer interaction by enabling computers to understand emotions.
  • Automated emotion recognition from multichannel EEG signals is a key research area.

Purpose of the Study:

  • To propose a novel approach for automatic emotion classification using multichannel EEG signals.
  • To investigate the efficacy of EEG multichannel rhythmic features combined with ensemble machine learning (EML) classifiers.
  • To evaluate the proposed method using leave-one-subject-out cross-validation (LOSOCV).

Main Methods:

  • Multivariate fast iterative filtering (MvFIF) was employed to analyze EEG rhythm sequences (delta, theta, alpha, beta, gamma).
  • Extracted features included Hjorth parameters and entropy measures from multichannel EEG rhythms.
  • Feature selection was performed using the minimum redundancy maximum relevance (mRMR) approach.
  • Ensemble machine learning classifiers, including subspace K-nearest neighbor (SS KNN), were utilized.

Main Results:

  • The proposed method achieved high classification accuracy, ranging from 93.5% to 99.8%, particularly with gamma rhythm multichannel features and EML-based SS KNN.
  • Comparisons with Support Vector Machine (SVM) and Artificial Neural Network (ANN) demonstrated the effectiveness of EML.
  • Analysis of multi-class emotions using an ensemble-based bagging tree on gamma rhythm showed promising results.

Conclusions:

  • Multichannel rhythmic features derived from EEG data, especially gamma rhythms, offer a powerful approach for automated emotion recognition.
  • Ensemble machine learning classifiers provide a robust framework for high-accuracy emotion classification from EEG.
  • This research presents a novel solution for analyzing multichannel rhythm-specific features in EEG data for affective computing.