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Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.

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Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
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Emotion recognition accuracy using electroencephalogram (EEG) signals improves with more channels and the Gamma frequency band. This study offers key insights for enhancing EEG-based emotion detection systems.

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Affective Computing

Background:

  • Emotion recognition from electroencephalogram (EEG) signals is a growing area of research.
  • Understanding the impact of signal processing parameters is crucial for accurate emotion detection.

Purpose of the Study:

  • To investigate how different frequency bands and channel configurations affect EEG-based emotion recognition accuracy.
  • To identify optimal parameters for improved emotion classification in valence and arousal dimensions.

Main Methods:

  • EEG data preprocessing and normalization were performed.
  • Discrete wavelet transform was used to divide signals into four frequency bands (theta, alpha, beta, gamma).
  • Entropy and energy features were extracted and classified using a K-nearest neighbor classifier.

Main Results:

  • Classification accuracy increased with the number of EEG channels used.
  • The Gamma frequency band yielded the highest accuracies, outperforming beta, alpha, and theta bands.
  • Accuracies for valence and arousal dimensions showed a consistent trend with increasing channels and frequency band efficacy.

Conclusions:

  • The study provides valuable references for selecting optimal frequency bands and channel numbers in EEG-based emotion recognition.
  • Findings contribute to the development of more effective emotion detection systems using neurophysiological data.