Abstract
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.
Highlights
An emotional state refers to a psychological and physiological state in which emotions and behaviors are interrelated and appraised within a context [1]
As the preprocessing methods are relatively general to a variety of EEG signal processing applications, here we focus on the feature extraction and emotion classification methods
We overviewed the computational methods used for emotional state estimation
Summary
An emotional state refers to a psychological and physiological state in which emotions and behaviors are interrelated and appraised within a context [1]. The development of an EEG-based emotion recognition system requires computational models that describe how the emotional state is represented in EEG signals and how one can estimate an emotional state from EEG signals. We feel needs for a review of the state-of-the-art computational models for emotional state estimation to subserve the development of advanced emotion recognition methods. This paper will review the current computational methods of emotional state estimation from the human EEG with discussion on challenges and some future directions. This paper will focus on the following aspects of EEG-based emotional state estimation models. It will start with a quick review on EEG correlates of emotion, including definition of the emotional state space, the design of emotional stimuli, and the EEG indices of emotion. We will propose a mathematical approach to the estimation of continuous emotional state based on Bayesian inference
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