Emotion Assessment by Variability-Based Ranking of Coherence Features from EEG

  • Iván De La Pava
  • Andres Álvarez-Meza
  • Alvaro-Angel Orozco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

The automatic assessment of emotional states has important applications in human-computer interfaces and marketing. Several approaches use a dimensional characterization of emotional states along with features extracted from physiological signals to classify emotions elicited from complex audiovisual stimuli; however, the classification accuracy remains low. Here, we develop an emotion assessment approach using a variability-based ranking scheme to reveal relevant coherence features from electroencephalography (EEG) signals. Our method achieves higher classification accuracies than comparable state-of-the-art methods and almost matches the performance of multimodal strategies that require information from several physiological signals.

Keywords

Emotion assessment Electroencephalography Coherence features Relevance analysis 

Notes

Acknowledgments

This work was supported by the project “Desarrollo de un sistema de apoyo al diagnóstico no invasivo de pacientes con epilepsia fármaco-resistente asociada a displasias corticales cerebrales: método costo-efectivo basado en procesamiento de imágenes de resonancia magnética” with code 1110-744-55778, and author I. De La Pava was supported by the program “Doctorado Nacional en Empresa - Convoctoria 758 de 2016”, both funded by Colciencias. The authors would also like to thank Cristian Alejandro Torres Valencia for his valuable contribution to this work.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Iván De La Pava
    • 1
  • Andres Álvarez-Meza
    • 1
  • Alvaro-Angel Orozco
    • 1
  1. 1.Automatic Research Group, Faculty of EngineeringUniversidad Tecnológica de PereiraPereiraColombia

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