Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
C2PS Center, Electrical Engineering and Computer Science Department, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates.
Sensors (Basel). 2023 May 28;23(11):5147. doi: 10.3390/s23115147.
It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to measure brain activities. This paper proposes an original framework for usability testing based on emotion detection using EEG signals, which can significantly affect software production and user satisfaction. This approach can provide an in-depth understanding of user satisfaction accurately and precisely, making it a valuable tool in software development. The proposed framework includes a recurrent neural network algorithm as a classifier, a feature extraction algorithm based on event-related desynchronization and event-related synchronization analysis, and a new method for selecting EEG sources adaptively for emotion recognition. The framework results are promising, achieving 92.13%, 92.67%, and 92.24% for the valence-arousal-dominance dimensions, respectively.
利用脑电图（EEG）脑信号来检测人类情绪正变得越来越有吸引力。EEG 是一种可靠且具有成本效益的技术，用于测量大脑活动。本文提出了一种基于使用 EEG 信号进行情感检测的可用性测试的原始框架，这对软件生产和用户满意度有重大影响。这种方法可以准确而精确地提供对用户满意度的深入了解，使其成为软件开发中的宝贵工具。所提出的框架包括作为分类器的递归神经网络算法、基于事件相关去同步和事件相关同步分析的特征提取算法，以及用于自适应选择 EEG 源以进行情感识别的新方法。该框架的结果很有前景，在效价唤醒主导度维度上分别达到了 92.13%、92.67%和 92.24%。
