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Related Experiment Video

Updated: Jun 19, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Emotion recognition from EEG using higher order crossings.

Panagiotis C Petrantonakis1, Leontios J Hadjileontiadis

  • 1Signal Processing and Biomedical Technology Unit, Telecommunications Laboratory, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR 541 24 Thessaloniki, Greece. ppetrant@auth.gr

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|October 28, 2009
PubMed
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This study introduces a new method for emotion recognition using electroencephalogram (EEG) signals, achieving high accuracy by adapting mirror neuron concepts for emotion induction and using higher-order crossings for feature extraction.

Area of Science:

  • Affective computing
  • Neuroscience
  • Biomedical engineering

Background:

  • Electroencephalogram (EEG)-based emotion recognition faces challenges in emotion induction and feature extraction for optimal classification.
  • Existing methods require complex feature engineering and robust classification strategies.

Purpose of the Study:

  • To present a novel technique for emotion evocation and EEG-based feature extraction for improved emotion recognition.
  • To adapt the mirror neuron system concept for efficient emotion induction through imitation.
  • To implement and evaluate a higher-order crossings (HOC)-based emotion classifier (HOC-EC).

Main Methods:

  • Emotion induction via facial expression imitation, leveraging the mirror neuron system.
  • Feature extraction using higher-order crossings (HOC) analysis.

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Last Updated: Jun 19, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

  • Classification using HOC-EC with Quadratic Discriminant Analysis (QDA), k-nearest neighbor, Mahalanobis distance, and Support Vector Machines (SVMs) on 3 EEG channels (Fp1, Fp2, F3/F4 bipolar).

    • Main Results:

    • HOC-EC achieved 62.3% accuracy (QDA) for single-channel and 83.33% accuracy (SVM) for combined-channel EEG data in recognizing six basic emotions.
    • Classification accuracy approached 100% when the number of emotion classes was reduced to five or fewer.
    • The HOC-EC method outperformed other feature extraction techniques.

    Conclusions:

    • The proposed HOC-EC method demonstrates significant efficiency for EEG-based emotion recognition.
    • The approach facilitates integration into human-machine interfaces, particularly in pervasive healthcare systems, to enhance affective computing.
    • This technology can provide valuable insights into user emotional status and trends.