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Familiarity effects in EEG-based emotion recognition.

Nattapong Thammasan1, Koichi Moriyama2, Ken-Ichi Fukui3

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Brain Informatics
|October 18, 2016
PubMed
Summary

Music familiarity significantly impacts electroencephalogram (EEG) brain activity. Unfamiliar music enhances the performance of EEG-based emotion recognition systems, suggesting its suitability for developing accurate emotion detection.

Keywords:
ClassificationElectroencephalogramFamiliarityMusic-emotion

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Area of Science:

  • Neuroscience
  • Affective Computing
  • Signal Processing

Background:

  • Emotion detection using electroencephalogram (EEG) is a growing research field.
  • Stimulus familiarity, a key subjective factor, has been largely overlooked in EEG-based emotion recognition.
  • Understanding subjective experiences like music familiarity is crucial for accurate emotion detection.

Purpose of the Study:

  • To investigate the influence of music familiarity on brain activity measured by EEG.
  • To evaluate the effectiveness of using familiar versus unfamiliar music for emotion recognition systems.
  • To determine if music familiarity impacts brainwave power spectra and functional connectivity.

Main Methods:

  • Utilized experimental data and the DEAP dataset for analysis.
  • Subjects selected both familiar and unfamiliar songs to elicit emotional responses.
  • Analyzed EEG signals, focusing on power spectra, functional connectivity, and fractal dimension.
  • Employed machine learning classifiers including Support Vector Machine, Multilayer Perceptron, and C4.5.

Main Results:

  • Music familiarity was found to influence brainwave power spectra and functional connectivity.
  • EEG-based emotion classification performance improved when using only low-familiarity music.
  • Features like fractal dimension and power spectral density were more effective with unfamiliar music.

Conclusions:

  • Music familiarity is a significant factor affecting brain activity and subjective emotional experience.
  • Unfamiliar music enhances the accuracy of EEG-based emotion classification systems.
  • Future emotion recognition systems should prioritize the use of unfamiliar stimuli for improved performance.