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Related Experiment Video

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Emotion Recognition in EEG Signals Using Decision Fusion Based Electrode Selection.

Himanshu Kumar1, Nagarajan Ganapathy2, Subha D Puthankattil3

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

Studies in Health Technology and Informatics
|May 27, 2021
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Summary

This study identifies emotional states using electroencephalogram (EEG) signals and a probabilistic random forest. Decision fusion effectively selected key electrodes for accurate emotion classification, improving arousal detection.

Keywords:
Decision FusionElectroencephalographyEmotionsProbabilistic Random Forest

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Machine Learning

Background:

  • Emotions significantly influence human cognitive functions like perception and concentration.
  • Electroencephalogram (EEG) signals offer insights into brain activity for emotion recognition.
  • Previous studies explored various brain lobes for emotion classification using EEG.

Purpose of the Study:

  • To identify emotional states by analyzing time-domain features from EEG signals.
  • To employ a probabilistic random forest and decision fusion for enhanced emotion classification.
  • To utilize Dempster-Shafer's (D-S) evidence theory for optimal electrode selection.

Main Methods:

  • Collected EEG signals from a public database, focusing on prefrontal and frontal electrodes (Fp1, Fp2, F3, F4, Fz).
  • Extracted eleven time-domain features from each electrode.
  • Applied a probabilistic random forest for feature analysis and Dempster-Shafer's (D-S) theory for decision fusion and electrode selection.

Main Results:

  • The proposed method successfully classified emotional states.
  • Decision fusion-based electrode selection achieved the highest accuracy for arousal classification (F-measure = 77.9%).
  • A combination of Fp2, F3, and F4 electrodes showed improved accuracy for arousal (65.1%) and valence (57.9%) dimensions.

Conclusions:

  • The developed method effectively classifies emotional states using EEG data.
  • Decision fusion is a valuable technique for selecting critical electrodes in emotion recognition.
  • The findings provide a pathway for more accurate and efficient emotion classification systems.