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EEG emotion recognition based on an innovative information potential index.

Atefeh Goshvarpour1, Ateke Goshvarpour2,3

  • 1Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Cognitive Neurodynamics
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Summary

Researchers developed a new method using electroencephalogram (EEG) signals to recognize emotions. This cross-information potential measure, particularly effective in the gamma frequency band, achieved over 90% accuracy in classifying emotional states.

Keywords:
Cross-information potentialElectroencephalogramEmotion recognitionTwo-dimensional emotion space

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Area of Science:

  • Neuroscience and Affective Computing
  • Signal Processing and Machine Learning

Background:

  • Growing demand for emotion recognition systems in clinical and non-clinical settings.
  • Electroencephalogram (EEG) signal analysis is a key approach for affect classification due to its direct link to brain activity.
  • Previous studies have explored brain connectivity for emotion recognition.

Purpose of the Study:

  • To propose a novel index quantifying brain electrode interactions for emotion characterization.
  • To evaluate the effectiveness of cross-information potential in EEG-based emotion recognition.
  • To identify optimal EEG frequency bands for emotion classification.

Main Methods:

  • A new measure based on cross-information potential between EEG electrode pairs was developed.
  • This measure was analyzed across different EEG frequency bands (delta, theta, alpha, beta, gamma).
  • Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) classifiers were used to categorize emotions in a valence-arousal space using the Database for Emotion Analysis using Physiological signals.

Main Results:

  • The kNN classifier achieved a maximum accuracy of 90.14%, sensitivity of 89.71%, and F-score of 94.57%.
  • The gamma frequency band demonstrated the highest emotion recognition rates.
  • Low valence-low arousal emotions were classified more accurately than other emotional states.

Conclusions:

  • Cross-information potential is a viable and effective measure for EEG-based emotion recognition.
  • The gamma frequency band holds significant potential for discriminating emotional states.
  • The proposed method offers a promising avenue for developing advanced affect classification systems.