Recognition of emotional states induced by music videos based on nonlinear feature extraction and SOM classification

• Published in: 2014 21th Iranian Conference on Biomedical Engineering (ICBME) pp. 333 - 337
• Main Authors: Hatamikia, S., Nasrabadi, A. M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2014
• Subjects: Accuracy; Biomedical engineering; Dunn index; Electroencephalography; Emotion recognition; Entropy; Feature extraction; Fractals; Nonlinear analysis; Self Organization Map (SOM); Sequential forward feature selection algorithm (SFS)
• DOI: 10.1109/ICBME.2014.7043946

This research aims at investigating the relationship between Electroencephalogram (EEG) signals and human emotional states. A subject-independent emotion recognition system is proposed using EEG signals collected during emotional audio-visual inductions to classify different classes of continuous valence-arousal model. First, four feature extraction methods based on Approximate Entropy, Spectral entropy, Katz's fractal dimension and Petrosian's fractal dimension were used; then, a two-stage feature selection method based on Dunn index and Sequential forward feature selection algorithm (SFS) algorithm was used to select the most informative feature subsets. Self-Organization Map (SOM) classifier was used to classify different emotional classes with the use of 5-fold cross-validation. The best results were achieved using combination of all features by average accuracies of %68.92 and %71.25 for two classes of valence and arousal, respectively. Furthermore, a hierarchical model which was constructed of two classifiers was used for classifying 4 emotional classes of valence and arousal levels and the average accuracy of %55.15 was achieved.