Fuzzy logic based emotion classification

Emotions affect many aspects of our daily lives including decision making, reasoning and physical wellbeing. Researchers have therefore addressed the detection of emotion from individuals' heart rate, skin conductance, pupil dilation, tone of voice, facial expression and electroencephalogram (EEG). This paper presents an algorithm for classifying positive and negative emotions from EEG. Unlike other algorithms that extract fuzzy rules from the data, the fuzzy rules used in this paper are obtained from emotion classification research reported in the literature and the classification output indicates both the type of emotion and its strength. The results show that the algorithm is more than 90 times faster than the widely used LIBSVM and the obtained average accuracy of 63.52 % is higher than previously reported using the same EEG dataset. This makes this algorithm attractive for real time emotion classification. In addition, the paper introduces a new oscillation feature computed from local minima and local maxima of the signal.

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