论文标题
通过基于模式学习的动态熵对情绪的跨个体识别，具有脑电图
Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features
论文作者
论文摘要
使用脑电图（EEG）和机器学习方法来识别情绪可以促进人类的计算机相互作用。但是，脑电图数据的类型构成了跨个体脑电图特征建模和分类的障碍。为了解决这个问题，我们提出了一个深入学习的框架，该框架表示为基于动态的基于熵的模式学习（DEPL），以抽象与多个个体中神经生理特征有关的信息指标。 DEPL通过建模基于动态熵的特征的皮质位置之间的相互依赖性来增强由深卷积神经网络产生的表示能力。 DEPL的有效性已通过两个公共数据库验证，通常称为DEAP和MAHNOB-HCI多模式标记数据库。具体而言，已应用一名主题训练和测试范例。关于脑电图情绪识别的许多实验表明，所提出的删除量优于传统的机器学习（ML）方法，并且可以在电极依赖关系之间学习W.R.T.不同的情绪，这对于通过适应现实世界应用中的人类情绪来开发有效的人类交互系统有意义。
Use of the electroencephalogram (EEG) and machine learning approaches to recognize emotions can facilitate affective human computer interactions. However, the type of EEG data constitutes an obstacle for cross-individual EEG feature modelling and classification. To address this issue, we propose a deep-learning framework denoted as a dynamic entropy-based pattern learning (DEPL) to abstract informative indicators pertaining to the neurophysiological features among multiple individuals. DEPL enhanced the capability of representations generated by a deep convolutional neural network by modelling the interdependencies between the cortical locations of dynamical entropy based features. The effectiveness of the DEPL has been validated with two public databases, commonly referred to as the DEAP and MAHNOB-HCI multimodal tagging databases. Specifically, the leave one subject out training and testing paradigm has been applied. Numerous experiments on EEG emotion recognition demonstrate that the proposed DEPL is superior to those traditional machine learning (ML) methods, and could learn between electrode dependencies w.r.t. different emotions, which is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.
