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EEG Analysis with Wavelet Transform under Music Perception Stimulation.

Jing Xue1

  • 1Xi'an University of Posts & Telecommunications, Shaanxi 710000, China.

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This summary is machine-generated.

This study introduces an EEG analysis method using wavelet transform for accurate emotional state assessment during music perception. The method achieves over 90% accuracy in classifying general emotions and shows promise for music therapy applications.

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Affective Computing

Background:

  • Accurate emotional state assessment is crucial for music therapy.
  • Existing methods for analyzing electroencephalogram (EEG) data for emotion recognition have limitations in accuracy and reliability.
  • Music perception is a powerful stimulus for eliciting emotional responses.

Purpose of the Study:

  • To propose an improved EEG analysis method for emotional state assessment using wavelet transform under music stimulation.
  • To enhance classification accuracy and reliability in emotion recognition.
  • To provide a foundation for supporting music therapy through objective emotional state evaluation.

Main Methods:

  • Utilized the DEAP database for multichannel EEG data.
  • Applied wavelet transform to extract alpha (α), beta (ß), and theta (θ) rhythms from specific EEG channels (frontal, temporal, central).
  • Employed Empirical Mode Decomposition (EMD) to obtain intrinsic mode function (IMF) components and extracted average energy and amplitude difference eigenvalues.
  • Implemented a support vector machine (SVM) classifier for emotional state evaluation.

Main Results:

  • Achieved over 90% correct classification rate for distinguishing between no emotion, positive emotion, and negative emotion.
  • Demonstrated superior classification accuracy (around 70%) compared to general feature extraction methods in pairwise emotion classification.
  • Identified significant correlations between EEG alpha (α) wave power and the polarity/intensity of emotions, with notable variations observed between specific emotional states (e.g., happiness vs. fear).

Conclusions:

  • The proposed EEG analysis method based on wavelet transform and EMD provides a highly accurate and reliable approach for emotional state assessment.
  • The findings highlight the significant role of EEG alpha (α) wave power in reflecting emotional states, offering valuable insights for psychological and physiological research.
  • This method holds considerable application potential in emotion perception research and practical applications, particularly in music therapy.