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Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.

Ante Topic1, Mladen Russo1, Maja Stella1

  • 1Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia.

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Summary

This study enhances Brain-Computer Interface (BCI) emotion recognition using holographic features and optimal electroencephalogram (EEG) channel selection. This approach achieves high accuracy in classifying emotions from EEG signals.

Keywords:
Brain-Computer InterfaceNeighborhood Component AnalysisReliefFcomputer-generated holographydeep learningelectroencephalogramgender specific emotion recognitionvalence-arousal-dominance model

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Artificial Intelligence and Machine Learning

Background:

  • Brain-Computer Interface (BCI) development requires accurate emotion recognition from electroencephalogram (EEG) signals.
  • EEG signals are inherently challenging due to non-stationarity, noise, and high dimensionality from numerous electrodes.
  • Current methods face limitations in accuracy, computational complexity, and subject comfort.

Purpose of the Study:

  • To develop an accurate and computationally efficient model for emotion recognition from EEG signals.
  • To investigate the effectiveness of holographic features and optimal electrode selection for improving BCI performance.
  • To reduce the number of electrodes required for reliable emotion detection.

Main Methods:

  • Implemented holographic features using computer-generated holography (CGH) to create 2D maps from EEG signal characteristics.
  • Utilized ReliefF and Neighborhood Component Analysis (NCA) for optimal electrode selection.
  • Employed Convolutional Neural Networks (CNNs) for feature extraction from the holographic maps.

Main Results:

  • Achieved state-of-the-art results in recognizing emotions within a three-dimensional emotional space (valence, arousal, dominance).
  • Demonstrated significant improvements in emotion recognition rates through channel selection methods.
  • Reported high accuracy: 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.

Conclusions:

  • Holographic features combined with optimal electrode selection offer a promising approach for robust EEG-based emotion recognition.
  • The methodology effectively reduces dimensionality and computational complexity while enhancing model accuracy.
  • The findings pave the way for more comfortable and accurate wearable BCI systems for emotion detection.