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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 573734, 13 pages
Review Article

A Review on the Computational Methods for Emotional State Estimation from the Human EEG

1Department of Brain and Cognitive Engineering, Korea University, Seoul 136701, Republic of Korea
2Samsung Electronics, DMC R&D Center, Suwon 443742, Republic of Korea
3Research and Business Foundation, Korea University, Seoul 136701, Republic of Korea

Received 11 January 2013; Accepted 18 February 2013

Academic Editor: Chang-Hwan Im

Copyright © 2013 Min-Ki Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.