Department of Brain and Cognitive Engineering, Korea University, Seoul 136701, Republic of Korea.
Comput Math Methods Med. 2013;2013:573734. doi: 10.1155/2013/573734. Epub 2013 Mar 24.
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.
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绪状态，从而建立情感人机交互。已经使用了各种方法来估计情绪状态，包括自我报告、惊跳反应、行为反应、自主测量和神经生理测量。其中，从脑电图（EEG）中推断情绪状态受到了相当大的关注，因为 EEG 可以以相对较低的成本和简单的方式直接反映情绪状态。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且嘈杂的多通道 EEG 数据中提取信息。在本文中，我们回顾了已经开发的用于提取情绪 EEG 指数、提取与情绪相关的特征或对 EEG 信号进行分类以归入多种情绪状态的计算方法。我们还提出使用序列贝叶斯推理来实时估计连续的情绪状态。我们提出了构建基于 EEG 的情绪识别系统的当前挑战，并提出了一些未来的方向。
