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Related Experiment Video

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Musical Emotions Recognition Using Entropy Features and Channel Optimization Based on EEG.

Zun Xie1, Jianwei Pan1, Songjie Li2

  • 1Department of Arts and Design, Anhui University of Technology, Ma'anshan 243002, China.

Entropy (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study introduces a novel method for analyzing brain responses to complex music-induced emotions using electroencephalogram (EEG) signals. Findings show that common EEG channels can effectively identify emotions, with the frontal lobe being a key emotional response area.

Keywords:
EEG signalschannel optimizationemotion recognitionentropymusical emotions

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Signal Processing

Background:

  • Current emotion recognition research often uses artificial stimuli, failing to capture complex, dynamic emotional responses.
  • Music, with its inherent dynamic emotional qualities, offers a more naturalistic stimulus for studying brain responses to emotion.

Purpose of the Study:

  • To investigate the brain's dynamic response to complex emotions evoked by long-term musical stimuli.
  • To develop and validate a method for emotion recognition using electroencephalogram (EEG) signals and non-linear feature extraction.
  • To identify a common set of EEG channels that represent universal emotional features across subjects.

Main Methods:

  • Utilized three long-term musical pieces with diverse emotional content as stimuli.
  • Applied Approximate Entropy (ApEn) and Sample Entropy (SampEn) to extract non-linear features from EEG signals.
  • Employed K-Nearest Neighbor (KNN) for emotion classification and Particle Swarm Optimization (PSO) for optimal channel selection.
  • Proposed a supervised feature dimensionality reduction method to identify common channels across subjects.

Main Results:

  • Emotion recognition accuracies exceeded 80% for simpler emotional categories and approximately 70% for more complex ones, using optimal channel sets.
  • Emotion recognition using a common channel set achieved accuracies about 10% lower than optimal sets but comparable to using all channels.
  • The frontal lobe showed a higher concentration of common channels, indicating its significant role in emotional processing.
  • Topographic maps revealed distinct entropy patterns across brain regions for different emotions.

Conclusions:

  • Long-term musical stimuli effectively evoke complex emotions and their dynamic brain responses.
  • A common channel set derived from optimal subject-specific channels can achieve efficient emotion recognition while reducing dimensionality.
  • The frontal lobe plays a crucial role in processing music-induced emotions.
  • This approach provides a foundation for developing more sophisticated brain-computer interfaces for emotion detection.