Begum Shahina, Barua Shaibal
School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
Stud Health Technol Inform. 2013;189:83-8.
Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preliminary result indicates that EEG sensor based classification could be an efficient technique for evaluation of the psychological state of individuals. Thus, the system can be used for personal health monitoring in order to improve users health.
脑电图（EEG）反映大脑活动，在生物医学研究中被广泛应用。然而，对该信号的分析仍然是一个具有挑战性的问题。本文提出了一种使用EEG信号评估压力的混合方法。它将多变量多尺度熵分析（MMSE）应用于数据级融合。基于案例的推理用于分类任务。我们的初步结果表明，基于EEG传感器的分类可能是评估个体心理状态的一种有效技术。因此，该系统可用于个人健康监测，以改善用户健康。
