PDF(Pubmed)
Abstract：
Personal identification systems based on electroencephalographic (EEG) signals have their own strengths and limitations. The stability of EEG signals strongly affects such systems. The human emotional state is one of the important factors that affects EEG signals\' stability. Stress is a major emotional state that affects individuals\' capability to perform day-to-day tasks. The main objective of this work is to study the effect of mental and emotional stress on such systems. Two experiments have been performed. In the first, we used hand-crafted features (time domain, frequency domain, and non-linear features), followed by a machine learning classifier. In the second, raw EEG signals were used as an input for the deep learning approaches. Different types of mental and emotional stress have been examined using two datasets, SAM 40 and DEAP. The proposed experiments proved that performing enrollment in a relaxed or calm state and identification in a stressed state have a negative effect on the identification system\'s performance. The best achieved accuracy for the DEAP dataset was 99.67% in the calm state and 96.67% in the stressed state. For the SAM 40 dataset, the best accuracy was 99.67%, 93.33%, 92.5%, and 91.67% for the relaxed state and stress caused by identifying mirror images, the Stroop color-word test, and solving arithmetic operations, respectively.
摘要：
基于脑电图（EEG）信号的个人识别系统具有其自身的优势和局限性。EEG信号的稳定性强烈地影响这样的系统。人的情绪状态是影响脑电信号稳定性的重要因素之一。压力是一种主要的情绪状态，影响个人执行日常任务的能力。这项工作的主要目的是研究心理和情绪压力对此类系统的影响。已经进行了两个实验。在第一,我们使用了手工制作的功能(时域，频域,和非线性特征)，其次是机器学习分类器。在第二个，原始EEG信号被用作深度学习方法的输入。已经使用两个数据集检查了不同类型的心理和情绪压力，SAM40和DEAP。所提出的实验证明，在放松或平静状态下进行注册和在压力状态下进行识别对识别系统的性能有负面影响。DEAP数据集的最佳准确度在平静状态下为99.67％，在压力状态下为96.67％。对于SAM40数据集，最佳准确度为99.67%,93.33%,92.5%,91.67%用于识别镜像引起的放松状态和压力，Stroop颜色词测试,并求解算术运算，分别。
