EEG-based subject-dependent emotion recognition algorithm using fractal dimension

In this paper, a real-time Electroencephalogram (EEG)-based emotion recognition algorithm using Higuchi Fractal Dimension (FD) Spectrum is proposed. As EEG is a nonlinear and multi-fractal signal, its FD spectrum can give a better understanding of the nonlinear property of EEG. Three values are sele...

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Bibliographic Details
Published inConference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 3166 - 3171
Main Authors Yisi Liu, Sourina, Olga
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2014
Subjects
Accuracy
affective computing
Classification algorithms
EEG
Electroencephalography
Emotion recognition
Emotiv Epoch
Feature extraction
Fractals
valence-arousal-dominance emotion model
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Summary:In this paper, a real-time Electroencephalogram (EEG)-based emotion recognition algorithm using Higuchi Fractal Dimension (FD) Spectrum is proposed. As EEG is a nonlinear and multi-fractal signal, its FD spectrum can give a better understanding of the nonlinear property of EEG. Three values are selected from the whole spectrum and are combined with the other features such as statistical and Higher Order Crossings ones. The Support Vector Machine is used as the classifier. The proposed algorithm is validated on both benchmark database DEAP with video stimuli and our own dataset which used visual stimuli to evoke emotions. Up to 8 emotions can be recognized with only 4 channels. The experiment analysis results show that using FD spectrum features it is possible to improve classification accuracy.
ISSN:1062-922X
DOI:10.1109/SMC.2014.6974415