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Green Image
Issue No. 02 - July-December (2010 vol. 1)
ISSN: 1949-3045
pp: 81-97
Panagiotis C. Petrantonakis , Aristotle University of Thessaloniki, Thessaloniki
Leontios J. Hadjileontiadis , Aristotle University of Thessaloniki, Thessaloniki
This paper aims at providing a new feature extraction method for a user-independent emotion recognition system, namely, HAF-HOC, from electroencephalograms (EEGs). A novel filtering procedure, namely, Hybrid Adaptive Filtering (HAF), for an efficient extraction of the emotion-related EEG-characteristics was developed by applying Genetic Algorithms to the Empirical Mode Decomposition-based representation of EEG signals. In addition, Higher Order Crossings (HOCs) analysis was employed for feature extraction realization from the HAF-filtered signals. The introduced HAF-HOC scheme incorporated four different classification methods to accomplish a robust emotion recognition performance. Through a series of facial-expression image projection, as a Mirror Neuron System-based emotion elicitation process, EEG data related to six basic emotions (happiness, surprise, anger, fear, disgust, and sadness) have been acquired from 16 healthy subjects using three EEG channels. Experimental results from the application of the HAF-HOC to the collected EEG data and comparison with previous approaches have shown that the HAF-HOC scheme clearly surpasses the latter in the field of emotion recognition from brain signals for the discrimination of up to six distinct emotions, providing higher classification rates up to 85.17 percent. The promising performance of the HAF-HOC surfaces the value of EEG signals within the endeavor of realizing more pragmatic, affective human-machine interfaces.
EEG, emotion recognition, EMD, genetic algorithms, higher order crossings analysis, hybrid adaptive filtering, mirror neuron system.

P. C. Petrantonakis and L. J. Hadjileontiadis, "Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis," in IEEE Transactions on Affective Computing, vol. 1, no. , pp. 81-97, 2010.
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