A review on the computational methods for emotional state estimation from the human EEG.
A review on the computational methods for emotional state estimation from the human EEG.
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DOI：
10.1155/2013/573734
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发表时间：
2013
影响因子：
--
通讯作者：
Kim SP
中科院分区：
文献类型：
--
作者：
Kim MK;Kim M;Oh E;Kim SP
越来越多的情感计算研究最近开发了一种计算机系统，该系统可以识别人类用户的情感状态以建立情感人机交互。已经使用各种测量来估计情绪状态，包括自我报告、惊吓反应、行为反应、自主测量和神经生理测量。其中，从脑电（EEG）中推断情绪状态因其能够以相对低的成本和简单的方式直接反映情绪状态而受到广泛关注。然而，基于EEG的情绪状态估计需要精心设计的计算方法来从复杂和嘈杂的多通道EEG数据中提取信息。在本文中，我们回顾了计算方法，已开发的扣除情绪的EEG指数，提取情绪相关的功能，或分类到许多情绪状态的EEG信号之一。我们还建议使用序列贝叶斯推理来估计连续的情绪状态在真实的时间。我们目前的挑战，建立一个基于EEG的情感识别系统，并提出了一些未来的发展方向。
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.
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