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Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset.

Erkan Deniz1, Nebras Sobahi2, Naaman Omar3

  • 1Electrical and Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey.

Health Information Science and Systems
|November 17, 2022
PubMed
Summary

This study introduces a novel approach for emotion recognition using electroencephalogram (EEG) signals, achieving 90.7% accuracy in classifying emotions like anger and happiness with Histogram of Oriented Gradients (HOG) features and a k-nearest neighbor (KNN) classifier.

Keywords:
EEG signalsEmotion recognitionICBrainDB datasetImage-based featuresSignal

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Emotion identification is crucial for advanced human-computer interaction systems.
  • Electroencephalogram (EEG) signals are extensively utilized for emotion recognition due to their direct neural correlates.
  • Existing EEG datasets have limitations, necessitating the use of novel datasets like ICBrainDB.

Purpose of the Study:

  • To classify four distinct emotions: angry, neutral, happy, and sad using the ICBrainDB EEG dataset.
  • To evaluate the efficacy of various signal and image processing techniques for EEG-based emotion recognition.
  • To identify optimal feature extraction and selection methods for accurate emotion classification.

Main Methods:

  • Extracted features from EEG signals using Wavelet Transform (WT), Tunable Q-factor Wavelet Transform (TQWT), Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Convolutional Neural Network (CNN).
  • Employed ReliefF for feature selection.
  • Utilized k-nearest neighbor (KNN), support vector machines, and neural networks for emotion classification, with tenfold cross-validation.

Main Results:

  • Achieved an average accuracy of 90.7% using HOG features combined with a KNN classifier.
  • Demonstrated the effectiveness of signal processing techniques (WT, TQWT) and image processing features (HOG, LBP) for EEG emotion recognition.
  • The ReliefF feature selector proved valuable in enhancing classification performance.

Conclusions:

  • The proposed methodology, particularly the combination of HOG features and KNN classifier, shows high potential for accurate EEG-based emotion recognition.
  • This research contributes a validated approach for classifying basic emotions from EEG data, paving the way for more sophisticated HCI applications.
  • The findings underscore the importance of advanced feature extraction and selection techniques in decoding complex human emotional states from neural signals.