Gonzalez Hector A, Yoo Jerald, Elfadel Ibrahim M
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:694-697. doi: 10.1109/EMBC.2019.8857248.
Emotion classification using EEG signal processing has the potential of significantly improving the social integration of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS) or the acute stages of Alzheimer's disease. One important challenge to the implementation of high-fidelity emotion recognition systems is the inadequacy of EEG data in terms of Signal-to-noise ratio (SNR), duration, and subject-to-subject variability. In this paper, we present a novel, integrated framework for semi-generic emotion detection using (1) independent component analysis for EEG preprocessing, (2) EEG subject clustering by unsupervised learning, and (3) a convolutional neural network (CNN) for EEG-based emotion recognition. The training and testing data was built using the combination of two publicly available repositories (DEAP and DREAMER), and a local dataset collected at Khalifa University using the standard International Affective Picture System (IAPS). The CNN classifier with the proposed transfer learning approach achieves an average accuracy of 70.26% for valence and 72.42% for arousal, which are superior to the reported accuracies of all generic (subject-independent) emotion classifiers.
利用脑电图（EEG）信号处理进行情绪分类，有潜力显著改善患有神经疾病（如肌萎缩侧索硬化症（ALS）或阿尔茨海默病急性期）患者的社会融合情况。实施高保真情绪识别系统的一个重要挑战是EEG数据在信噪比（SNR）、持续时间和个体差异方面存在不足。在本文中，我们提出了一个新颖的集成框架，用于半通用情绪检测，该框架使用（1）独立成分分析进行EEG预处理，（2）通过无监督学习对EEG个体进行聚类，以及（3）基于EEG的情绪识别卷积神经网络（CNN）。训练和测试数据是通过结合两个公开可用的数据库（DEAP和DREAMER）以及在哈里发大学使用标准国际情感图片系统（IAPS）收集的本地数据集构建的。采用所提出的迁移学习方法的CNN分类器在效价方面的平均准确率达到70.26%，在唤醒度方面达到72.42%，优于所有通用（独立于个体）情绪分类器所报告的准确率。
