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.
翻译:使用电子脑图和机器学习方法来识别情感,可以促进人的情感互动;然而,电子脑图和机器学习方法的种类数据构成了跨个体 EEG特征建模和分类的障碍;为解决这一问题,我们提议了一个深学习框架,其用意是动态的英特罗比模式学习(DEPL),用于与多个个人的神经生理特征有关的抽象信息指标。DEPL通过模拟动态英特基特征的交替地点之间的交替性,增强了由深共振神经网络产生的表达能力。DEPL的有效性已经由两个公共数据库(通常称为DEAP和MAHNOB-HCI)验证,这两个公共数据库称为DEAP和MAHNOB-HCI多式标记数据库。具体地说,将一个专题留出培训和测试模式已经应用。关于EEG情感认知的许多实验表明,拟议的DEP优于这些传统的机器学习方法,可以学习不同的电子依赖性情感,这对通过适应现实世界中的人类情感,开发有效的人体计算机互动系统具有意义。