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Updated: Oct 20, 2025

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Positive and Negative Emotion Classification Based on Multi-channel.

Fangfang Long1, Shanguang Zhao2, Xin Wei3,4

  • 1Department of Psychology, Nanjing University, Nanjing, China.

Frontiers in Behavioral Neuroscience
|September 13, 2021
PubMed
Summary
This summary is machine-generated.

Electroencephalography (EEG) signals effectively differentiate emotions using energy ratios and differential entropy. Machine learning models, particularly Support Vector Machines (SVM), achieve over 86% accuracy in emotion classification with optimized EEG channel selection.

Keywords:
EEGback propagation neural networkdecision treeemotion classificationk-nearest neighborsupport vector machine

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Emotion recognition is crucial for understanding human affective states.
  • Electroencephalography (EEG) offers a non-invasive method for capturing brain activity related to emotions.

Purpose of the Study:

  • To investigate the efficacy of EEG features for classifying positive and negative emotions.
  • To determine optimal EEG channel configurations for accurate emotion recognition.

Main Methods:

  • Collected multi-channel EEG data from 26 subjects using emotionally evocative videos.
  • Extracted frequency band features, including energy ratio and differential entropy.
  • Employed Support Vector Machine (SVM) classifier for emotion classification.
  • Performed channel selection to optimize model performance.

Main Results:

  • Energy ratio and differential entropy effectively classified emotions.
  • Using only forehead channels yielded 66% accuracy.
  • Utilizing all channels improved accuracy to 82%.
  • Channel selection resulted in a model achieving over 86% classification accuracy.

Conclusions:

  • EEG-based emotion classification is feasible and accurate.
  • SVM classifiers combined with selected EEG channels provide a robust method for emotion recognition.
  • Forehead EEG channels offer valuable insights but comprehensive data enhances performance.